{"id":"W6932012480","doi":"10.5683/sp3/4ijspf","title":"Presqu'ile (West) Ontario. 1:50,000. Map Sheet 030N13, ed. 1, 1951","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Natural (archaeology); Digital mapping; Geographic information system; Aerial photography; Government (linguistics); Orthophoto","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":[0.0004042891,0.001465429,0.001082389,0.003302036,0.001214866,0.002553281,0.001391166,0.0005626043,0.1154561],"category_scores_gemma":[0.002787102,0.0007821461,0.0007337498,0.01422082,0.0004907692,0.0008132887,0.0009169094,0.0008002014,0.07883998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009522729,"about_ca_system_score_gemma":0.01687606,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9422156,"about_ca_topic_score_gemma":0.9661852,"domain_scores_codex":[0.9994643,0.00002601878,0.00004794891,0.000131709,0.0001959698,0.0001340293],"domain_scores_gemma":[0.9983468,0.0001094933,0.0001581354,0.0001726621,0.001023654,0.0001892544],"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":[0.00002828828,0.000003137024,0.00132001,0.0003877855,0.00001396152,0.00001479585,0.0000373043,0.00008345299,0.00003316337,0.0002478157,0.9944647,0.003365532],"study_design_scores_gemma":[0.00005507517,0.000003971895,0.01947568,0.0002674671,0.00001625248,0.00002784265,0.0001259513,0.00008054672,0.00008039208,0.0001858555,0.9796663,0.00001470868],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008956228,0.0001128695,0.0000225755,0.00003988275,0.00001460026,0.00000622151,0.9974916,0.00007098864,0.002151665],"genre_scores_gemma":[0.001258359,0.0003431322,0.0001926972,0.00004033914,0.00001056586,0.00004665283,0.9895941,0.00008033289,0.00843385],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1154561,"threshold_uncertainty_score":0.386239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05004410928059859,"score_gpt":0.3154388644380432,"score_spread":0.2653947551574446,"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."}}