{"id":"W4212948447","doi":"10.1002/ece3.8616","title":"Extracting spatial networks from capture–recapture data reveals individual site fidelity patterns within a marine mammal’s spatial range","year":2022,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Université du Québec en Outaouais; University of Lethbridge","funders":"Ministère des Forêts, de la Faune et des Parcs","keywords":"Habitat; Range (aeronautics); Beluga Whale; Population; Ecology; Wildlife; Geography; Spatial ecology; Occupancy; Mark and recapture; Spatial variability; Home range; Spatial analysis; Negative binomial distribution; Statistics; Arctic; Biology; Remote sensing","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":[0.001733272,0.0003259366,0.0003282558,0.002505763,0.000305853,0.0006258416,0.0005044626,0.0003196595,0.0006650282],"category_scores_gemma":[0.007129616,0.0003616147,0.0007522122,0.001414902,0.0002838522,0.0008142886,0.000651043,0.000363882,0.0002860258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005619118,"about_ca_system_score_gemma":0.0003762464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02679587,"about_ca_topic_score_gemma":0.04875118,"domain_scores_codex":[0.9994131,0.0002194643,0.00004898709,0.0002053339,0.00006575465,0.00004739062],"domain_scores_gemma":[0.996174,0.002027673,0.0009278837,0.0004769634,0.0002828308,0.0001104925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000577662,0.00006649331,0.855759,0.0001329897,0.0003745564,0.0001283563,0.0003688229,0.09499187,0.002114885,0.001120763,0.0008894823,0.04399508],"study_design_scores_gemma":[0.000009057761,0.00007523617,0.5205994,0.00005226059,0.0001133537,0.0002524433,0.0005032828,0.4735909,0.0009291322,0.002241226,0.001598707,0.00003509219],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9385271,0.0003158898,0.05723458,0.0001331877,0.00001152054,0.00004702322,0.002189535,0.0002242326,0.001316968],"genre_scores_gemma":[0.9767351,0.0001401015,0.02071067,0.0000205668,0.00000806249,0.00003788081,0.002138951,0.00001647059,0.0001922552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02679587,"threshold_uncertainty_score":0.05327982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084448953875371,"score_gpt":0.2334739884495307,"score_spread":0.212629498910777,"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."}}