[docs] Fix various syntax and rendering errors (elastic#127062) (elastic#127344)

* fix syntax and rendering errors

* clean up

* fix versions

* more clean up

* more fixes

* more fixes

* more fixes

(cherry picked from commit 08552f1)

# Conflicts:
#	docs/reference/elasticsearch/mapping-reference/dense-vector.md
#	docs/reference/elasticsearch/mapping-reference/semantic-text.md

Co-authored-by: Colleen McGinnis <[email protected]>

Author: leemthompo

docs/docset.yml

@@ -35,6 +35,7 @@ toc:
   - toc: release-notes
   - toc: extend
 subs:
+  version: 9.0.0
   api-request-title:   "Request"
   api-prereq-title:   "Prerequisites"
   api-description-title:   "Description"
@@ -103,8 +104,8 @@ subs:
   transforms-cap:   "Transforms"
   dfanalytics-cap:   "Data frame analytics"
   dfanalytics:   "data frame analytics"
-  dfanalytics-job:   "'{dataframe} analytics job'"
-  dfanalytics-jobs:   "'{dataframe} analytics jobs'"
+  dfanalytics-job:   "data frame analytics job"
+  dfanalytics-jobs:   "data frame analytics jobs"
   ctransforms:   "continuous transforms"
   regression:   "regression"
   regression-cap:   "Regression"

docs/reference/aggregations/search-aggregations-bucket-adjacency-matrix-aggregation.md

@@ -94,14 +94,12 @@ The response contains buckets with document counts for each filter and combinati
 `filters`
 :   (Required, object) Filters used to create buckets.
 
-    ::::{dropdown} Properties of `filters`
-    `<filter>`
-    :   (Required, [Query DSL object](/reference/query-languages/querydsl.md)) Query used to filter documents. The key is the filter name.
+::::{dropdown} Properties of filters
+`<filter>`
+:   (Required, [Query DSL object](/reference/query-languages/querydsl.md)) Query used to filter documents. The key is the filter name.
 
-        At least one filter is required. The total number of filters cannot exceed the [`indices.query.bool.max_clause_count`](/reference/elasticsearch/configuration-reference/search-settings.md#indices-query-bool-max-clause-count) setting. See [Filter limits](#adjacency-matrix-agg-filter-limits).
-
-
-    ::::
+    At least one filter is required. The total number of filters cannot exceed the [`indices.query.bool.max_clause_count`](/reference/elasticsearch/configuration-reference/search-settings.md#indices-query-bool-max-clause-count) setting. See [Filter limits](#adjacency-matrix-agg-filter-limits).
+::::
 
 
 `separator`

docs/reference/aggregations/search-aggregations-bucket-categorize-text-aggregation.md

@@ -26,7 +26,8 @@ The algorithm used for categorization was completely changed in version 8.3.0. A
 
     The `categorization_analyzer` field can be specified either as a string or as an object. If it is a string it must refer to a [built-in analyzer](/reference/text-analysis/analyzer-reference.md) or one added by another plugin. If it is an object it has the following properties:
 
-    :::::{dropdown} Properties of `categorization_analyzer`
+    **Properties of `categorization_analyzer`**:
+
     `char_filter`
     :   (array of strings or objects) One or more [character filters](/reference/text-analysis/character-filter-reference.md). In addition to the built-in character filters, other plugins can provide more character filters. This property is optional. If it is not specified, no character filters are applied prior to categorization. If you are customizing some other aspect of the analyzer and you need to achieve the equivalent of `categorization_filters` (which are not permitted when some other aspect of the analyzer is customized), add them here as [pattern replace character filters](/reference/text-analysis/analysis-pattern-replace-charfilter.md).
 
@@ -41,8 +42,6 @@ The algorithm used for categorization was completely changed in version 8.3.0. A
     `filter`
     :   (array of strings or objects) One or more [token filters](/reference/text-analysis/token-filter-reference.md). In addition to the built-in token filters, other plugins can provide more token filters. This property is optional. If it is not specified, no token filters are applied prior to categorization.
 
-    :::::
-
 
 `categorization_filters`
 :   (Optional, array of strings) This property expects an array of regular expressions. The expressions are used to filter out matching sequences from the categorization field values. You can use this functionality to fine tune the categorization by excluding sequences from consideration when categories are defined. For example, you can exclude SQL statements that appear in your log files. This property cannot be used at the same time as `categorization_analyzer`. If you only want to define simple regular expression filters that are applied prior to tokenization, setting this property is the easiest method. If you also want to customize the tokenizer or post-tokenization filtering, use the `categorization_analyzer` property instead and include the filters as `pattern_replace` character filters.

docs/reference/aggregations/search-aggregations-bucket-correlation-aggregation.md

@@ -11,43 +11,33 @@ A sibling pipeline aggregation which executes a correlation function on the conf
 
 ## Parameters [bucket-correlation-agg-syntax]
 
-`buckets_path`
-:   (Required, string) Path to the buckets that contain one set of values to correlate. For syntax, see [`buckets_path` Syntax](/reference/aggregations/pipeline.md#buckets-path-syntax).
+### `buckets_path`
 
-`function`
-:   (Required, object) The correlation function to execute.
+(Required, string) Path to the buckets that contain one set of values to correlate. For syntax, see [`buckets_path` Syntax](/reference/aggregations/pipeline.md#buckets-path-syntax).
 
-    ::::{dropdown} Properties of `function`
-    `count_correlation`
-    :   (Required*, object) The configuration to calculate a count correlation. This function is designed for determining the correlation of a term value and a given metric. Consequently, it needs to meet the following requirements.
+### `function`
 
-        * The `buckets_path` must point to a `_count` metric.
-        * The total count of all the `bucket_path` count values must be less than or equal to `indicator.doc_count`.
-        * When utilizing this function, an initial calculation to gather the required `indicator` values is required.
+(Required, object) The correlation function to execute.
 
-        ::::{dropdown} Properties of `count_correlation`
-        `indicator`
-        :   (Required, object) The indicator with which to correlate the configured `bucket_path` values.
+#### `count_correlation`
 
-            ::::{dropdown} Properties of `indicator`
-            `doc_count`
-            :   (Required, integer) The total number of documents that initially created the `expectations`. It’s required to be greater than or equal to the sum of all values in the `buckets_path` as this is the originating superset of data to which the term values are correlated.
+(Required*, object) The configuration to calculate a count correlation. This function is designed for determining the correlation of a term value and a given metric. Consequently, it needs to meet the following requirements.
 
-            `expectations`
-            :   (Required, array) An array of numbers with which to correlate the configured `bucket_path` values. The length of this value must always equal the number of buckets returned by the `bucket_path`.
+* The `buckets_path` must point to a `_count` metric.
+* The total count of all the `bucket_path` count values must be less than or equal to `indicator.doc_count`.
+* When utilizing this function, an initial calculation to gather the required `indicator` values is required.
 
-            `fractions`
-            :   (Optional, array) An array of fractions to use when averaging and calculating variance. This should be used if the pre-calculated data and the `buckets_path` have known gaps. The length of `fractions`, if provided, must equal `expectations`.
+`indicator`
+:   (Required, object) The indicator with which to correlate the configured `bucket_path` values.
 
-            ::::
-
-
-        ::::
-
-
-    ::::
+    `doc_count`
+    :   (Required, integer) The total number of documents that initially created the `expectations`. It’s required to be greater than or equal to the sum of all values in the `buckets_path` as this is the originating superset of data to which the term values are correlated.
 
+    `expectations`
+    :   (Required, array) An array of numbers with which to correlate the configured `bucket_path` values. The length of this value must always equal the number of buckets returned by the `bucket_path`.
 
+    `fractions`
+    :   (Optional, array) An array of fractions to use when averaging and calculating variance. This should be used if the pre-calculated data and the `buckets_path` have known gaps. The length of `fractions`, if provided, must equal `expectations`.
 
 ## Syntax [_syntax_8]
 

docs/reference/aggregations/search-aggregations-bucket-significantterms-aggregation.md

@@ -196,7 +196,7 @@ Picking a free-text field as the subject of a significant terms analysis can be
 ::::
 
 
-::::{admonition} Use the *"like this but not this"* pattern
+::::{admonition} Use the "like this but not this" pattern
 You can spot mis-categorized content by first searching a structured field e.g. `category:adultMovie` and use significant_terms on the free-text "movie_description" field. Take the suggested words (I’ll leave them to your imagination) and then search for all movies NOT marked as category:adultMovie but containing these keywords. You now have a ranked list of badly-categorized movies that you should reclassify or at least remove from the "familyFriendly" category.
 
 The significance score from each term can also provide a useful `boost` setting to sort matches. Using the `minimum_should_match` setting of the `terms` query with the keywords will help control the balance of precision/recall in the result set i.e a high setting would have a small number of relevant results packed full of keywords and a setting of "1" would produce a more exhaustive results set with all documents containing *any* keyword.

docs/reference/aggregations/search-aggregations-bucket-significanttext-aggregation.md

@@ -265,7 +265,7 @@ The numbers returned for scores are primarily intended for ranking different sug
 ::::
 
 
-::::{admonition} Use the *"like this but not this"* pattern
+::::{admonition} Use the "like this but not this" pattern
 You can spot mis-categorized content by first searching a structured field e.g. `category:adultMovie` and use significant_text on the text "movie_description" field. Take the suggested words (I’ll leave them to your imagination) and then search for all movies NOT marked as category:adultMovie but containing these keywords. You now have a ranked list of badly-categorized movies that you should reclassify or at least remove from the "familyFriendly" category.
 
 The significance score from each term can also provide a useful `boost` setting to sort matches. Using the `minimum_should_match` setting of the `terms` query with the keywords will help control the balance of precision/recall in the result set i.e a high setting would have a small number of relevant results packed full of keywords and a setting of "1" would produce a more exhaustive results set with all documents containing *any* keyword.

docs/reference/aggregations/search-aggregations-change-point-aggregation.md

@@ -46,15 +46,14 @@ A `change_point` aggregation looks like this in isolation:
 `bucket`
 :   (Optional, object) Values of the bucket that indicates the discovered change point. Not returned if no change point was found. All the aggregations in the bucket are returned as well.
 
-    ::::{dropdown} Properties of bucket
+    **Properties of `bucket**:
+
     `key`
     :   (value) The key of the bucket matched. Could be string or numeric.
 
     `doc_count`
     :   (number) The document count of the bucket.
 
-    ::::
-
 
 `type`
 :   (object) The found change point type and its related values. Possible types:

docs/reference/aggregations/search-aggregations-metrics-geocentroid-aggregation.md

@@ -201,7 +201,7 @@ POST /places/_search?size=0
 }
 ```
 
-::::{admonition} Using `geo_centroid` as a sub-aggregation of `geohash_grid`
+::::{admonition} Using geo_centroid as a sub-aggregation of geohash_grid
 :class: warning
 
 The [`geohash_grid`](/reference/aggregations/search-aggregations-bucket-geohashgrid-aggregation.md) aggregation places documents, not individual geopoints, into buckets. If a document’s `geo_point` field contains [multiple values](/reference/elasticsearch/mapping-reference/array.md), the document could be assigned to multiple buckets, even if one or more of its geopoints are outside the bucket boundaries.

docs/reference/aggregations/search-aggregations-metrics-max-aggregation.md

@@ -10,7 +10,7 @@ mapped_pages:
 A `single-value` metrics aggregation that keeps track and returns the maximum value among the numeric values extracted from the aggregated documents.
 
 ::::{note}
-The `min` and `max` aggregation operate on the `double` representation of the data. As a consequence, the result may be approximate when running on longs whose absolute value is greater than `2`^`53`^.
+The `min` and `max` aggregation operate on the `double` representation of the data. As a consequence, the result may be approximate when running on longs whose absolute value is greater than `2^53`.
 ::::
 
 

docs/reference/aggregations/search-aggregations-metrics-min-aggregation.md

@@ -10,7 +10,7 @@ mapped_pages:
 A `single-value` metrics aggregation that keeps track and returns the minimum value among numeric values extracted from the aggregated documents.
 
 ::::{note}
-The `min` and `max` aggregation operate on the `double` representation of the data. As a consequence, the result may be approximate when running on longs whose absolute value is greater than `2`^`53`^.
+The `min` and `max` aggregation operate on the `double` representation of the data. As a consequence, the result may be approximate when running on longs whose absolute value is greater than `2^53`.
 ::::