{"id":"W2148389197","doi":"10.1111/j.1365-2656.2009.01613.x","title":"Considering ecological dynamics in resource selection functions","year":2009,"lang":"en","type":"review","venue":"Journal of Animal Ecology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":277,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Lakehead University; University of Saskatchewan","funders":"","keywords":"Resource (disambiguation); Selection (genetic algorithm); Ecology; Resource distribution; Abundance (ecology); Variety (cybernetics); Logistic function; Competition (biology); Function (biology); Computer science; Biology; Resource allocation; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007487571,0.0002108578,0.001000754,0.0002285346,0.0001104314,0.00001463731,0.0002234722,0.0006421352,0.001359496],"category_scores_gemma":[0.0002990347,0.0001799464,0.0002565343,0.0004048026,0.0001330207,0.0001562671,0.00008490906,0.001031666,0.0001920834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001795129,"about_ca_system_score_gemma":0.0001698964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007831545,"about_ca_topic_score_gemma":0.001766919,"domain_scores_codex":[0.9979969,0.0004113051,0.0009432493,0.0002286818,0.0001223884,0.0002975082],"domain_scores_gemma":[0.9984983,0.0004440615,0.0008785562,0.00008165153,0.00001842293,0.00007896843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002837839,0.001100037,0.2625142,0.0004344646,0.000197279,0.0007245467,0.0000502396,0.0007972827,0.000003320351,0.0006563355,0.03223064,0.7010078],"study_design_scores_gemma":[0.0002465252,0.001824997,0.188958,0.0002332173,0.0002190988,0.001933959,0.00004183967,0.0002581557,5.633652e-8,0.0002221211,0.8058701,0.0001919824],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.1559675,0.8173122,0.0001562038,0.003235979,0.001600034,0.001331069,0.00001363134,0.00008046117,0.02030289],"genre_scores_gemma":[0.006231463,0.9911273,0.0008433938,0.0009147424,0.0003064367,0.00001913684,0.00001445366,0.0000272052,0.0005158868],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7736394,"threshold_uncertainty_score":0.9995534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02889197107645333,"score_gpt":0.2753645208688769,"score_spread":0.2464725497924235,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}