{"id":"W6949549900","doi":"10.5281/zenodo.1326856","title":"Bayesian Triple Collocation","year":2018,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Historical Geography and Cartography","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Bayesian probability; Collocation (remote sensing); Bayes estimator; Feature (linguistics); Noise (video); Representation (politics)","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":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007100868,0.000146061,0.0001655987,0.0005453252,0.003220086,0.0004632127,0.0009735368,0.0002360703,0.1400416],"category_scores_gemma":[0.0003655201,0.0001654333,0.0001120864,0.001440155,0.0005813835,0.00008377027,0.0002186944,0.0001790339,0.01706555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001369438,"about_ca_system_score_gemma":0.000009506358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004284165,"about_ca_topic_score_gemma":0.00003038196,"domain_scores_codex":[0.997947,0.0005714073,0.0001683073,0.0003953631,0.0005416704,0.0003761893],"domain_scores_gemma":[0.9988943,0.00001207802,0.0001453968,0.0003762498,0.0003272446,0.0002447295],"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":[0.000009070792,0.00005591549,0.000001374933,0.00002069244,0.00003093039,0.00000200868,0.00141261,1.071974e-7,0.00001129948,0.007828881,0.9582554,0.03237168],"study_design_scores_gemma":[0.0001692177,0.00009227329,0.00001341736,0.00003801315,0.00001980677,0.000001913576,0.0004800563,0.000003231661,0.000003454097,0.0002290723,0.9987618,0.0001877021],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000004506001,0.0002301624,0.00269398,0.000294594,0.0004331989,0.0004631057,0.000120171,0.001490756,0.9942695],"genre_scores_gemma":[0.005970349,0.0007210377,0.0003251702,0.0001640569,0.001896453,1.184229e-7,0.001574361,0.006041594,0.9833069],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.122976,"threshold_uncertainty_score":0.9980776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357970144333614,"score_gpt":0.2595653420974924,"score_spread":0.2359856406541563,"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."}}