suppressing duplicate in bib and updating production.bib

Author: jchiquet

_bibliography/in_production.bib

@@ -1,16 +1,15 @@
-@article{favrot_hierarchical,
+@article{adrat_repulsion,
   bibtex_show = {true},
-  author      = {Favrot, Armand and Makoswki, David},
-  title       = {{A hierarchical model to evaluate pest treatments from prevalence and intensity data}},
+  author      = {Adrat, Hamza and Decreusefond, Laurent},
+  title       = {{Point Process Discrimination According to Repulsion}},
   journal     = {Computo},
-  year        = 2023,
-  abstract    = {In plant epidemiology, pest abundance is measured in field trials using metrics assessing either pest prevalence (fraction of the plant population infected) or pest intensity (average number of pest individuals present in infected plants). Some of these trials rely on prevalence, while others rely on intensity, depending on the protocols. In this paper, we present a hierarchical Bayesian model able to handle both types of data. In this model, the intensity and prevalence variables are derived from a latent variable representing the number of pest individuals on each host individual, assumed to follow a Poisson distribution. Effects of pest treaments, time trend, and between-trial variability are described using fixed and random effects. We apply the model to a real dataset in the context of aphid control in sugar beet fields. In this dataset, prevalence and intensity were derived from aphid counts observed on either factorial trials testing different types of pesticides treatments or field surveys monitoring aphid abundance.  Next, we perform simulations to assess the impacts of using either prevalence or intensity data, or both types of data simultaneously, on the accuracy of the model parameter estimates and on the ranking of pesticide treatment efficacy. Our results show that, when pest prevalence and pest intensity data are collected separately in different trials, the model parameters are more accurately estimated using both types of trials than using one type of trials only. When prevalence data are collected in all trials and intensity data are collected in a subset of trials, estimations and pest treatment ranking are more accurate using both types of data than using prevalence data only. When only one type of observation can be collected in a pest survey or in an experimental trial, our analysis indicates that it is better to collect intensity data than prevalence data when all or most of the plants are expected to be infested, but that both types of data lead to similar results when the level of infestation is low to moderate. Finally, our simulations show that it is unlikely to obtain accurate results with fewer than 40 trials when assessing the efficacy of pest control treatments based on prevalence and intensity data. Because of its flexibility, our model can be used to evaluate and rank the efficacy of pest treatments using either prevalence or intensity data, or both types of data simultaneously. As it can be easily implemented using standard Bayesian packages, we hope that it will be useful to agronomists, plant pathologists, and applied statisticians to analyze pest surveys and field experiments conducted to assess the efficacy of pest treatments.},
-  doi =		 {10.57750/6cgk-g727},
-  repository  = {published-202312-favrot-hierarchical},
+  year        = 2024,
+  abstract    = {In numerous applications, cloud of points do seem to exhibit repulsion in the intuitive sense that there is no local cluster as in a Poisson process. Motivated by data coming from cellular networks, we devise a classification algorithm based on the form of the Voronoi cells. We show that, in the particular set of data we are given, we can retrieve some repulsiveness between antennas, which was expected for engineering reasons.},
+  doi         = {10.57750/3r07-aw28},
+  repository  = {published_202401_adrat_repulsion},
   type        = {{Research article}},
-  language    = {R},
+  language    = {Python},
   domain      = {Statistics},
-  keywords    = {bayesian model, epidemiology, hierarchical model, pest control, trial, survey},
-  issn =	 {2824-7795},  
+  keywords    = {classification, point process, repulsion},
+  issn        = {2824-7795}
 }
-

_bibliography/published.bib

@@ -1,20 +1,3 @@
-@article{adrat_repulsion,
-  bibtex_show = {true},
-  author      = {Adrat, Hamza and Decreusefond, Laurent},
-  title       = {{Point Process Discrimination According to Repulsion}},
-  journal     = {Computo},
-  year        = 2024,
-  abstract    = {In numerous applications, cloud of points do seem to exhibit repulsion in the intuitive sense that there is no local cluster as in a Poisson process. Motivated by data coming from cellular networks, we devise a classification algorithm based on the form of the Voronoi cells. We show that, in the particular set of data we are given, we can retrieve some repulsiveness between antennas, which was expected for engineering reasons.},
-  doi         = {10.57750/3r07-aw28},
-  repository  = {published_202401_adrat_repulsion},
-  type        = {{Research article}},
-  language    = {Python},
-  domain      = {Statistics},
-  keywords    = {classification, point process, repulsion},
-  issn        = {2824-7795}
-}
-
-
 
 @article{favrot_hierarchical,
   bibtex_show = {true},