{"id":"W4221003180","doi":"10.22541/au.164865115.53827873/v1","title":"Solving the Sample Size Problem for Resource Selection Analysis","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Memorial University of Newfoundland; University of Saskatchewan; University of Guelph","funders":"","keywords":"Woodland caribou; Selection (genetic algorithm); Sample size determination; Ecology; Sample (material); Resource (disambiguation); Habitat; Tundra; Woodland; Temperate climate; Boreal; Environmental resource management; Computer science; Geography; Ecosystem; Statistics; Mathematics; Environmental science; Biology; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0718626,0.001843579,0.002694055,0.002721581,0.0015945,0.00261376,0.004007959,0.003165764,0.006056233],"category_scores_gemma":[0.3301451,0.001583449,0.002226993,0.003008896,0.004211117,0.005680599,0.003980028,0.006806997,0.001347616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002142725,"about_ca_system_score_gemma":0.00421492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003369111,"about_ca_topic_score_gemma":0.00307942,"domain_scores_codex":[0.9529874,0.03713744,0.00178582,0.003655519,0.003869253,0.0005644811],"domain_scores_gemma":[0.6515765,0.3214969,0.004817738,0.01526237,0.005946982,0.0008995917],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008731846,0.0003432224,0.01648234,0.001826904,0.001168344,0.001090723,0.001267274,0.2758366,0.00540618,0.3603334,0.02231505,0.3130569],"study_design_scores_gemma":[0.0003381924,0.0002295936,0.001566805,0.0001949506,0.00009042289,0.0003297809,0.0001436364,0.5567617,0.001636076,0.4319996,0.006654338,0.00005478999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002521266,0.0002041471,0.995265,0.000892493,0.00008990405,0.0001786157,0.0001290358,0.0002271357,0.0004923144],"genre_scores_gemma":[0.07517524,0.0004258104,0.9184546,0.0009688803,0.0004746002,0.002378524,0.0005717083,0.0003277711,0.00122276],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9281374,"threshold_uncertainty_score":0.3800503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01466183877895343,"score_gpt":0.238787546971523,"score_spread":0.2241257081925696,"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."}}