{"id":"W4416220649","doi":"10.1101/2025.11.13.688337","title":"Including fitness and health proxies can alter our understanding of habitat selection","year":2025,"lang":"","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; McGill University; University of British Columbia; Nature Conservancy of Canada; Environment and Climate Change Canada; Fisheries and Oceans Canada","funders":"British Columbia Knowledge Development Fund; Nunavut Wildlife Management Board; Fisheries and Oceans Canada; Environment and Climate Change Canada","keywords":"Habitat; Selection (genetic algorithm); Resource (disambiguation); Inference; Natural selection; Function (biology)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00428306,0.0004286636,0.0004990658,0.001352139,0.000318102,0.001756228,0.0004744963,0.0005227604,0.002001904],"category_scores_gemma":[0.01179389,0.0002076442,0.0005327945,0.001044341,0.001520553,0.002048097,0.0007531806,0.0008021183,0.0001890607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007764661,"about_ca_system_score_gemma":0.0004036277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004468855,"about_ca_topic_score_gemma":0.007597898,"domain_scores_codex":[0.9979534,0.001310624,0.0001405365,0.0003042284,0.0002030177,0.00008808535],"domain_scores_gemma":[0.990935,0.006153081,0.001477112,0.000900745,0.0003621496,0.0001718737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003741717,0.0001799854,0.7806584,0.000667479,0.001185596,0.000437313,0.001109547,0.03695975,0.00964404,0.02628877,0.002346099,0.1401489],"study_design_scores_gemma":[0.0000204625,0.0002744092,0.8974923,0.0002400062,0.0003852572,0.0003088537,0.001010634,0.04998732,0.003646826,0.03789355,0.008649452,0.00009097444],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9144349,0.003118399,0.06324804,0.004016059,0.0001056606,0.00005996477,0.001541201,0.0002270371,0.0132488],"genre_scores_gemma":[0.9920586,0.0004047997,0.006546081,0.0003761845,0.00004408009,0.00001562033,0.000176046,0.00002783553,0.0003507444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004468855,"threshold_uncertainty_score":0.02265126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04394154624235339,"score_gpt":0.2750321906442709,"score_spread":0.2310906444019175,"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."}}