{"id":"W3109674256","doi":"10.48550/arxiv.2011.14070","title":"Movement Tracks for the Automatic Detection of Fish Behavior in Videos","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Fish <Actinopterygii>; Trajectory; Identification (biology); Artificial intelligence; Key (lock); Animal behavior; Deep learning; Machine learning; Fishery; Ecology; Biology; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000216346,0.000161553,0.0001973166,0.00006447981,0.00006288272,0.00001622848,0.0007639815,0.0001891449,0.00004065841],"category_scores_gemma":[0.0000714112,0.0001511398,0.0001276464,0.0002396518,0.0001887652,0.00008590003,0.001040729,0.0003100435,0.00001530109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003996714,"about_ca_system_score_gemma":0.00001217631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001185667,"about_ca_topic_score_gemma":0.0004648966,"domain_scores_codex":[0.999009,0.00005261744,0.000210307,0.0004581526,0.00008594178,0.0001840278],"domain_scores_gemma":[0.9991027,0.0001270663,0.0001942491,0.0005340729,0.000009444115,0.00003248631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002984841,0.001090704,0.4120256,0.0009672352,0.0002770501,0.0001856949,0.002849847,0.4834037,0.05507108,0.001878666,0.001048922,0.04090296],"study_design_scores_gemma":[0.0009373848,0.0002937575,0.6646907,0.0001256431,0.0003402732,8.231239e-7,0.00124304,0.1988999,0.09201344,0.04046471,0.0003689057,0.0006214491],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855138,0.00000444293,0.01282705,0.0003074525,0.0002173915,0.0009060769,0.00002617282,0.0001463529,0.00005126294],"genre_scores_gemma":[0.9994583,0.00002355968,0.0003154764,0.00004181966,0.00001631324,0.00002141276,0.000003389093,0.00001244611,0.0001072908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2845038,"threshold_uncertainty_score":0.61633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1002515529789959,"score_gpt":0.2122438684281202,"score_spread":0.1119923154491244,"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."}}