{"id":"W2587142359","doi":"10.1111/2041-210x.12755","title":"Characterizing change points and continuous transitions in movement behaviours using wavelet decomposition","year":2017,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Segmentation; Pattern recognition (psychology); Computer science; Temporal scales; Scale (ratio); Spatial ecology; Movement (music); Artificial intelligence; Discrete wavelet transform; Wavelet; Wavelet transform; Tracking (education); Biological system; Ecology; Geography; Cartography; Physics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0005250148,0.0004553619,0.0003518521,0.002327802,0.000161493,0.0006293641,0.0003729656,0.0004986925,0.0004985368],"category_scores_gemma":[0.001737366,0.0001531892,0.0005410105,0.001344364,0.000315988,0.000434739,0.0004620084,0.000445008,0.0003223704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002495635,"about_ca_system_score_gemma":0.0002321813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004045921,"about_ca_topic_score_gemma":0.003516459,"domain_scores_codex":[0.9996287,0.00004588038,0.00003828364,0.0001293698,0.0001065817,0.00005125355],"domain_scores_gemma":[0.9992204,0.0003195331,0.0001661028,0.00008689816,0.0001559722,0.00005118335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007227835,0.0003014354,0.0883264,0.0007232155,0.0003465588,0.0007562753,0.0009119199,0.1640243,0.1476629,0.002599907,0.004120524,0.5895038],"study_design_scores_gemma":[0.00002109255,0.000187254,0.1954577,0.0000966343,0.00009128757,0.0003481057,0.0004973765,0.7807199,0.01660137,0.002383935,0.003537918,0.00005742202],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7793714,0.000618539,0.216388,0.000134994,0.00005931168,0.00008154774,0.001379999,0.0006077218,0.00135855],"genre_scores_gemma":[0.9177705,0.0004222146,0.07845466,0.00002840464,0.00002459002,0.0000778857,0.002460746,0.00004926273,0.0007116472],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004045921,"threshold_uncertainty_score":0.008044779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04468741263333936,"score_gpt":0.3512048008427131,"score_spread":0.3065173882093737,"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."}}