{"id":"W3091735433","doi":"10.1002/ecs2.3238","title":"An introduction to event history analyses for ecologists","year":2020,"lang":"en","type":"article","venue":"Ecosphere","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Event (particle physics); Data science; Population; Ecology; Computer science; Range (aeronautics); Biology; Sociology; Demography; Engineering","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":["insufficient_payload"],"category_scores_codex":[0.00009506109,0.00006048043,0.00007520893,0.000003914772,0.00006063034,0.000004846153,0.0001161103,0.00004589798,0.01488615],"category_scores_gemma":[0.00009586457,0.00006070298,0.00003099375,0.00007088067,0.00002884923,0.0001488278,0.00002805942,0.00004240952,0.001341198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002622042,"about_ca_system_score_gemma":0.00001337232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000399269,"about_ca_topic_score_gemma":0.0005739643,"domain_scores_codex":[0.9994352,0.00003102853,0.0001039258,0.0002569964,0.00005491687,0.0001178758],"domain_scores_gemma":[0.9997185,0.00001855093,0.00003901772,0.000119897,0.000006266786,0.00009778433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004289312,0.0000418651,0.02871768,0.000002899946,0.000006146156,7.187994e-7,0.0002148163,0.01182819,0.002414772,0.00007686832,0.9535799,0.003073263],"study_design_scores_gemma":[0.0001403918,0.0003681333,0.4432905,4.367384e-7,0.00001314984,5.972687e-7,0.00006926018,0.004695077,0.000406159,0.0001570637,0.5507566,0.0001025992],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612641,0.00002236941,0.006491318,0.02884686,0.0003734535,0.0002654078,0.000002555163,0.00006288861,0.002671024],"genre_scores_gemma":[0.9859909,0.000001285266,0.004121792,0.007784533,0.0004491772,0.00006473729,0.00001620562,0.000007247527,0.00156412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4145728,"threshold_uncertainty_score":0.9994364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0413032184747348,"score_gpt":0.2880687258181795,"score_spread":0.2467655073434448,"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."}}