{"id":"W212698334","doi":"","title":"Assessing cumulative human impacts on northern woodland caribou with traditional ecological knowledge and resource selection functions","year":2010,"lang":"en","type":"article","venue":"The Mathematics Enthusiast","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Environment; University of Montana","keywords":"Woodland caribou; Selection (genetic algorithm); Woodland; Geography; Human use; Resource (disambiguation); Ecology; Environmental resource management; Cumulative effects; Environmental science; Computer science; Biology; Habitat; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002986384,0.0001163541,0.0001009609,0.00002551745,0.0006868026,0.00006468899,0.00008852596,0.00006784786,0.0006625894],"category_scores_gemma":[0.00005950403,0.00007051692,0.00002160565,0.0001376608,0.0003014996,0.0001459827,0.00004070298,0.0003020221,0.00007544032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005865144,"about_ca_system_score_gemma":0.00001510953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002146305,"about_ca_topic_score_gemma":0.005857092,"domain_scores_codex":[0.999338,0.00006066921,0.0001337924,0.0001688616,0.0001426977,0.0001559605],"domain_scores_gemma":[0.9993683,0.0003365361,0.00009891736,0.000126413,0.00001261558,0.00005729588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001294275,0.004418811,0.7701517,0.00007516691,0.0002453452,0.00001685555,0.1708133,0.005412999,0.01571,0.02241564,0.006568654,0.004042042],"study_design_scores_gemma":[0.0002831468,0.0001839129,0.9940263,0.00001598371,0.00003917268,0.00006808271,0.0008474138,0.003139794,0.00007159893,0.0008827391,0.0003243857,0.00011748],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9616089,0.00000144927,0.0003038158,0.0003599234,0.00004299178,0.0001843885,0.000004129891,0.00003727282,0.03745712],"genre_scores_gemma":[0.9990245,2.958231e-7,0.0004209123,0.0001214823,0.00007660285,0.00002650642,0.00001658361,0.00001002881,0.0003030598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2238746,"threshold_uncertainty_score":0.725489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03361715662547467,"score_gpt":0.2636855006070998,"score_spread":0.2300683439816251,"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."}}