{"id":"W4242584175","doi":"10.22541/au.158636346.65395646","title":"A framework for validating noninvasive genetic spatial capture-recapture studies for rare and elusive species","year":2020,"lang":"en","type":"dataset","venue":"Authorea","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Alberta Environment and Protected Areas; Trent University","funders":"Trent University; Government of Alberta","keywords":"Statistics; Sampling (signal processing); Mark and recapture; Sample size determination; Range (aeronautics); Population; Population size; Boreal; Environmental science; Econometrics; Ecology; Mathematics; Computer science; Biology; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001177154,0.0003221586,0.0004476049,0.00003035669,0.0003692468,0.00004407333,0.0002627738,0.0005125747,0.0001993085],"category_scores_gemma":[0.001666788,0.0002929247,0.000109346,0.00009858577,0.0002627415,0.00007153288,0.0002856698,0.0003303345,0.00007344947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009320505,"about_ca_system_score_gemma":0.00003945609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001473113,"about_ca_topic_score_gemma":0.001405402,"domain_scores_codex":[0.9985584,0.0000648928,0.0002902479,0.0006095782,0.0001653146,0.0003115383],"domain_scores_gemma":[0.9984003,0.0009263262,0.0003000888,0.0002471748,0.00003618774,0.00008991754],"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":[0.00005991268,0.00001668807,0.00141314,0.0001676185,0.0001270405,0.00001454397,0.001255513,0.00002707942,0.00001454776,0.00007360263,0.996286,0.0005442515],"study_design_scores_gemma":[0.0004568339,0.000310723,0.01592878,0.000149147,0.000455227,0.00001579492,0.001492766,0.0001064161,0.00008359312,0.01118223,0.969296,0.0005224465],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002011704,0.0003664357,0.006751225,0.003435157,0.0007651309,0.001625134,0.9849932,0.00002634423,0.00002562656],"genre_scores_gemma":[0.001987667,0.0003348906,0.02493429,0.004452757,0.001277538,0.00108524,0.9655284,0.00004585214,0.0003533208],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02699002,"threshold_uncertainty_score":0.9999523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03826162807979613,"score_gpt":0.2893981846255728,"score_spread":0.2511365565457766,"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."}}