{"id":"W1558690717","doi":"","title":"Detection of stationary animals in deep-sea video","year":2013,"lang":"en","type":"article","venue":"2013 OCEANS - San Diego","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Enviro Neptune (Canada); University of Victoria","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Remote sensing; Pattern recognition (psychology); Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002267567,0.0003165845,0.0002831684,0.001445349,0.0001226459,0.0003234612,0.0003078208,0.0003801251,0.0005208227],"category_scores_gemma":[0.0006672019,0.0001248637,0.0001771514,0.0006276389,0.0001678463,0.0003546069,0.0003202155,0.0001810783,0.0002903363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001798792,"about_ca_system_score_gemma":0.0001638425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002718197,"about_ca_topic_score_gemma":0.004979767,"domain_scores_codex":[0.9998442,0.00001540008,0.000007431921,0.00004634809,0.00005898056,0.00002769791],"domain_scores_gemma":[0.999653,0.00009389722,0.00006864134,0.00003048417,0.000113314,0.00004072325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004133152,0.0001786732,0.05652867,0.000259887,0.00008537173,0.0006570796,0.0002153354,0.01641959,0.4713237,0.0004737094,0.001546412,0.4518983],"study_design_scores_gemma":[0.00002868844,0.0004729026,0.2612157,0.00005237881,0.00008752055,0.000600923,0.0003388679,0.5744547,0.1585415,0.0007746603,0.003388855,0.00004334767],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8792443,0.0005319828,0.1157106,0.00008470951,0.00005799191,0.00006771069,0.0006955527,0.0008515955,0.002755589],"genre_scores_gemma":[0.9469865,0.0002864324,0.04990144,0.00004107572,0.00002702416,0.00002008742,0.001000762,0.00002928436,0.001707509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002718197,"threshold_uncertainty_score":0.00540477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01849756201338166,"score_gpt":0.2434619321422081,"score_spread":0.2249643701288264,"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."}}