{"id":"W4281661115","doi":"10.1101/2022.06.01.494350","title":"Effective conservation decisions require models designed for purpose: a case study for boreal caribou in Ontario’s Ring of Fire","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Environment and Climate Change Canada; Canadian Forest Service; University of British Columbia; Wilfrid Laurier University","funders":"Environment and Climate Change Canada","keywords":"Woodland caribou; Environmental resource management; Wildlife; Selection (genetic algorithm); Transparency (behavior); Habitat; Population; Resource (disambiguation); Geography; Boreal; Environmental science; Computer science; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002207064,0.0005334531,0.000312239,0.0004560602,0.001741208,0.001187735,0.001188822,0.0008785697,0.002006234],"category_scores_gemma":[0.007721171,0.0002912726,0.0004875691,0.0008550786,0.0008808509,0.0008621156,0.0006497199,0.0006584423,0.0001382961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008672033,"about_ca_system_score_gemma":0.005087343,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8104547,"about_ca_topic_score_gemma":0.9157079,"domain_scores_codex":[0.999416,0.0003101006,0.00002689209,0.00007171565,0.00007358745,0.0001016098],"domain_scores_gemma":[0.9951957,0.003710658,0.0001796716,0.0002619721,0.0004588076,0.0001932697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003337268,0.00051267,0.2582566,0.0001733433,0.0001424198,0.003527213,0.002788825,0.6917583,0.001442905,0.007704171,0.006196724,0.02716315],"study_design_scores_gemma":[0.0001527215,0.0002204935,0.06283377,0.00007185008,0.0001082482,0.0003823308,0.005803795,0.912248,0.001305114,0.004614559,0.012193,0.00006613864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891216,0.0001486148,0.004386241,0.0007512266,0.00001360868,0.00008368786,0.0005844882,0.0001200991,0.004790381],"genre_scores_gemma":[0.9865006,0.0001317001,0.01090592,0.00005660264,0.000009117334,0.0000454086,0.0004372262,0.00004266265,0.001870804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1895453,"threshold_uncertainty_score":0.3813232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0316984748223845,"score_gpt":0.2484276757358386,"score_spread":0.2167292009134541,"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."}}