{"id":"W7117884291","doi":"10.5539/ijsp.v14n4p48","title":"Time-Frequency Segmentation of Northern Fur Seal Diving Behavior Using Autoregressive Spectral Analysis","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics and Probability","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive model; Foraging; Segmentation; Exploratory analysis; Spectral analysis; Fur seal; Movement (music); Harbor seal","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004361738,0.0002241957,0.0001774375,0.001028785,0.0001590737,0.0003362501,0.000157776,0.0002126972,0.0004546811],"category_scores_gemma":[0.001179731,0.0001126973,0.0003517507,0.0005185686,0.0001891472,0.0003418571,0.0002179862,0.0001830284,0.0001659233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001448747,"about_ca_system_score_gemma":0.0002401927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005519112,"about_ca_topic_score_gemma":0.007711032,"domain_scores_codex":[0.9998642,0.00003751601,0.0000097546,0.00004214443,0.00003054492,0.00001578198],"domain_scores_gemma":[0.9996717,0.0001583292,0.00007073255,0.00002812216,0.00005627589,0.00001477519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003043249,0.0001623131,0.1113144,0.0002183044,0.0002172885,0.0003411832,0.00137907,0.1658909,0.1075595,0.00608542,0.002174866,0.6043525],"study_design_scores_gemma":[0.000008577437,0.0001380363,0.2833743,0.00005187935,0.00006213861,0.0002145085,0.0005926879,0.7001413,0.007553787,0.00514702,0.002656245,0.00005943432],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5963612,0.0005147604,0.4003368,0.000107978,0.00002595134,0.00002907094,0.0003184353,0.0003079899,0.001997776],"genre_scores_gemma":[0.9431379,0.000206769,0.05555018,0.00001267226,0.00002047103,0.00002734393,0.0004399942,0.00003534332,0.0005694057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005519112,"threshold_uncertainty_score":0.01097399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01709895093290451,"score_gpt":0.2962672077985525,"score_spread":0.279168256865648,"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."}}