{"id":"W4384830729","doi":"10.24132/jwscg.2023.1","title":"Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data","year":2023,"lang":"en","type":"article","venue":"Journal of WSCG","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Centre National de la Recherche Scientifique; Ministère de l'Enseignement Supérieur et de la Recherche; Max-Planck-Gesellschaft; Agence Nationale Des Parcs Nationaux; Ministère de l'Enseignement Supérieur et de la Recherche Scientifique","keywords":"Pipeline (software); Scale-invariant feature transform; Artificial intelligence; Computer science; Convolutional neural network; Identification (biology); Computer vision; Pattern recognition (psychology); Set (abstract data type); Data set; Similarity (geometry); Feature extraction; Image (mathematics); Biology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001165312,0.00005952888,0.0001281786,0.00009868359,0.00005403432,0.00001353017,0.0004590067,0.00004550557,0.001003254],"category_scores_gemma":[0.0002587198,0.00005103459,0.00003582728,0.0002622782,0.00007553949,0.0003263342,0.0001044333,0.0001070739,0.0002288519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004438717,"about_ca_system_score_gemma":0.00002922384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001278689,"about_ca_topic_score_gemma":0.00002173832,"domain_scores_codex":[0.9988707,0.00009474196,0.0004496077,0.0001072223,0.0003736333,0.0001041375],"domain_scores_gemma":[0.9990315,0.0001970123,0.0004310899,0.0002884767,0.00001593818,0.00003600503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005019211,0.0001801268,0.9066854,0.00002506204,0.00004721528,0.00002504011,0.0004133935,0.002560987,0.006770506,0.00002038522,0.07668782,0.006533881],"study_design_scores_gemma":[0.0003248224,0.0001037107,0.9680942,0.00002655861,0.00003344836,0.000004400895,0.00009143207,0.0298256,0.0008001104,0.0003641741,0.0002845516,0.00004700383],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964958,0.000005462044,0.0002462556,0.002762247,0.0002334514,0.00006252492,0.00002673774,0.00001291744,0.0001546101],"genre_scores_gemma":[0.9988953,0.000007478411,0.0002792169,0.000509422,0.00005492933,0.000001280567,0.000043728,0.00000513365,0.0002035292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07640327,"threshold_uncertainty_score":0.9999099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04608377269381757,"score_gpt":0.2699160001082172,"score_spread":0.2238322274143996,"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."}}