{"id":"W3124191085","doi":"","title":"Predicting fine-scale distributions of gray wolves: is habitat an effective surrogate for prey availability?","year":2007,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Predation; Gray (unit); Gray wolf; Habitat; Geography; Ecology; Statistics; Cartography; Artificial intelligence; Environmental science; Computer science; Mathematics; Biology; Canis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001169678,0.0002378502,0.0003092619,0.0004681629,0.0001360156,0.0005882044,0.0003008231,0.0003127348,0.0005331394],"category_scores_gemma":[0.00404488,0.0001803725,0.0002099475,0.0004027403,0.0002254366,0.0005590965,0.00034662,0.0002359308,0.0002229125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003677427,"about_ca_system_score_gemma":0.000271788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0273319,"about_ca_topic_score_gemma":0.06475819,"domain_scores_codex":[0.9997948,0.00009400221,0.00001057517,0.00005898152,0.00001959076,0.00002211301],"domain_scores_gemma":[0.9980125,0.001274058,0.0003194748,0.0001425,0.0001036568,0.0001477591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005947801,0.00002446335,0.9716715,0.000008363929,0.00004355516,0.00002545393,0.00004270368,0.01075774,0.0004450912,0.0001037745,0.0002144146,0.01660344],"study_design_scores_gemma":[0.00001763149,0.0001121806,0.8349335,0.00001839167,0.00004318733,0.00008774511,0.0002505284,0.1625134,0.0006358201,0.0009271536,0.0004487926,0.00001167341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963785,0.0001006963,0.002805894,0.00007570481,0.000001971952,0.000006407847,0.0001649996,0.00002126301,0.0004445989],"genre_scores_gemma":[0.9962459,0.00007068713,0.003100952,0.00001482848,0.000004144109,0.00000556936,0.0003067582,0.000005010238,0.0002460404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0273319,"threshold_uncertainty_score":0.05434561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009205238037697988,"score_gpt":0.2307678104364982,"score_spread":0.2215625723988002,"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."}}