{"id":"W4285086063","doi":"10.3389/fmars.2022.816794","title":"Predicting Seabird Foraging Habitat for Conservation Planning in Atlantic Canada: Integrating Telemetry and Survey Data Across Thousands of Colonies","year":2022,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; Memorial University of Newfoundland; University of Manitoba; Dalhousie University; University of New Brunswick; Fisheries and Oceans Canada; Environment and Climate Change Canada; Mount Allison University; Birds Canada","funders":"Fonds en Fiducie pour la Faune du Nouveau-Brunswick; Environment and Climate Change Canada; Mount Allison University","keywords":"Foraging; Seabird; Range (aeronautics); Habitat; Species distribution; Ecology; Aerial survey; Charadrius; Distance sampling; Bathymetry; Geography; Conservation biology; Biology; Predation; Remote sensing; Cartography","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":[0.0008269784,0.0003408418,0.0003391543,0.0009696198,0.0006434004,0.0009238163,0.0008892743,0.0003069236,0.0005312883],"category_scores_gemma":[0.003128862,0.000296286,0.0004010857,0.00151845,0.0003765359,0.0008093112,0.000591294,0.0005263288,0.0001371167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005743462,"about_ca_system_score_gemma":0.004617918,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9429063,"about_ca_topic_score_gemma":0.9702928,"domain_scores_codex":[0.9997692,0.00004098253,0.00001504659,0.00007641255,0.00004429227,0.00005406171],"domain_scores_gemma":[0.9989513,0.0003022812,0.0001572245,0.0001068746,0.0003535184,0.0001287016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004053482,0.00004607384,0.9119031,0.00002768139,0.0001248846,0.00004750249,0.0002384134,0.05469249,0.0006497262,0.0001603441,0.0007443413,0.03132486],"study_design_scores_gemma":[0.00001071348,0.00002530832,0.7087262,0.00003783482,0.00007280504,0.00003237755,0.000764588,0.2879494,0.0003782896,0.0004574672,0.001517667,0.00002732664],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932288,0.0002001631,0.004323708,0.0002424322,0.000006376952,0.00001607431,0.00131866,0.00007749604,0.0005863219],"genre_scores_gemma":[0.9901452,0.0001665898,0.007281555,0.00004503877,0.00000371443,0.00001168444,0.002001154,0.00001395211,0.000331166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05709374,"threshold_uncertainty_score":0.1148599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03495476836991981,"score_gpt":0.2887059425101648,"score_spread":0.2537511741402451,"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."}}