{"id":"W2309339938","doi":"","title":"Aggregated Sidestripe Shrimp Catch Grid 1997-2004","year":2005,"lang":"en","type":"article","venue":"downloadable data","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Shrimp; Fishery; Fishing; Grid; Geography; Environmental science; Computer science; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003596933,0.0004553576,0.0004090386,0.002453143,0.0001952014,0.000494475,0.0006669864,0.000232666,0.01244707],"category_scores_gemma":[0.001405288,0.0002316374,0.000324464,0.004212689,0.00007553312,0.0003162569,0.0005951486,0.0003492885,0.007972887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000802066,"about_ca_system_score_gemma":0.001090547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06636019,"about_ca_topic_score_gemma":0.0828895,"domain_scores_codex":[0.9995186,0.00003805267,0.00005261178,0.0001533581,0.0001845084,0.00005271122],"domain_scores_gemma":[0.9990376,0.00006437291,0.0002027618,0.000150789,0.0004650763,0.00007931835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008137574,0.0001506358,0.2645352,0.0004819404,0.0004293145,0.0004188747,0.0003017372,0.008130593,0.002160341,0.001391175,0.6733959,0.04779048],"study_design_scores_gemma":[0.0000724433,0.00006845862,0.7542518,0.00007094518,0.00004423787,0.0001822118,0.0002652597,0.003942994,0.001138804,0.0002544228,0.2396813,0.00002722257],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04630895,0.00005161442,0.000333218,0.00006636367,0.00003615194,0.00005137693,0.9473902,0.0002059454,0.00555622],"genre_scores_gemma":[0.02896424,0.00005581751,0.0007780752,0.00003097101,0.00001141798,0.0001024546,0.9655648,0.00003626476,0.004455889],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06636019,"threshold_uncertainty_score":0.1319478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03111240552339359,"score_gpt":0.2681768731664487,"score_spread":0.2370644676430551,"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."}}