{"id":"W4360610593","doi":"10.1186/s40462-023-00377-2","title":"Linking movement and dive data to prey distribution models: new insights in foraging behaviour and potential pitfalls of movement analyses","year":2023,"lang":"en","type":"article","venue":"Movement Ecology","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pacific Institute for Climate Solutions; University of Victoria; University of Manitoba; University of Windsor; University of British Columbia; Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada; British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Nunavut Wildlife Management Board; ArcticNet; Canada Research Chairs; Office of Naval Research; Canada Foundation for Innovation; Weston Family Foundation; Polar Knowledge Canada","keywords":"Animal ecology; Foraging; Movement (music); Predation; Ecology; Distribution (mathematics); Biology; Geography; Mathematics","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":[0.03081312,0.001181255,0.001309178,0.003111155,0.0006657518,0.003201047,0.001784203,0.001386613,0.001539982],"category_scores_gemma":[0.1137316,0.0008501285,0.001257771,0.002731371,0.001277578,0.005079411,0.002531038,0.002060252,0.0005333727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066423,"about_ca_system_score_gemma":0.001369264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02822896,"about_ca_topic_score_gemma":0.03249459,"domain_scores_codex":[0.9902521,0.006877875,0.0006504809,0.001156767,0.0008738872,0.0001888667],"domain_scores_gemma":[0.9010294,0.08243919,0.004442297,0.007744325,0.003707959,0.0006368828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002424186,0.0001759118,0.6956602,0.0008805506,0.001499672,0.0003120111,0.001797292,0.08745395,0.001761008,0.01047078,0.00407743,0.1956688],"study_design_scores_gemma":[0.00004204183,0.0002373813,0.2496525,0.001676482,0.0002462253,0.0007468364,0.002089598,0.6712144,0.001357467,0.05991582,0.01260612,0.0002151157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4498253,0.01148442,0.5151835,0.00984662,0.0005140504,0.0002304661,0.003347163,0.002027109,0.007541331],"genre_scores_gemma":[0.8430998,0.002879613,0.1494954,0.0006861759,0.000267528,0.0001339449,0.001933829,0.0003971312,0.001106568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03081312,"threshold_uncertainty_score":0.1629574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07240259961234836,"score_gpt":0.3024536853394539,"score_spread":0.2300510857271056,"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."}}