{"id":"W2006844559","doi":"10.1139/f00-151","title":"Prediction and the aquatic sciences","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Predictive modelling; Set (abstract data type); Computer science; Data science; SPARK (programming language); Probabilistic logic; Management science; Artificial intelligence; Machine learning; Engineering; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00173152,0.00008501875,0.0001471568,0.00006641867,0.001223391,0.0001889289,0.0002653438,0.00003933163,0.00144729],"category_scores_gemma":[0.0002643415,0.00004801171,0.00002792503,0.0003478243,0.007061816,0.0005123323,0.00002449823,0.0001025446,0.00002387861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003388561,"about_ca_system_score_gemma":0.0002114228,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006385321,"about_ca_topic_score_gemma":0.02948167,"domain_scores_codex":[0.99902,0.00009492614,0.0002545223,0.0001417061,0.0002332836,0.0002555277],"domain_scores_gemma":[0.9993669,0.0002052788,0.000150172,0.0000576467,0.000005022495,0.0002149968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009945778,0.000007154489,0.9762663,0.000001683038,0.000006468641,0.00002309056,0.00198252,0.00002504471,0.00002708218,0.0009393052,0.004145165,0.01656622],"study_design_scores_gemma":[0.002291562,0.001868171,0.8402716,0.0001126911,0.0002060272,0.002314291,0.01577852,0.05203246,0.00008489806,0.06065735,0.02390615,0.0004762838],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836168,0.0003343308,0.0001507773,0.008279935,0.00027759,0.00009141687,6.875474e-7,0.000002456083,0.007245959],"genre_scores_gemma":[0.9980887,0.0001111461,0.0005903124,0.0007544453,0.00004320224,0.000002282978,1.150363e-7,0.000002222397,0.00040755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1359947,"threshold_uncertainty_score":0.9994655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02315519489508299,"score_gpt":0.2112528196684848,"score_spread":0.1880976247734018,"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."}}