{"id":"W6931887375","doi":"10.5683/sp3/bh1xlu","title":"Charlton Station (West) Ontario. 1:50,000. Map Sheet 041P16, ed. 1, 1958","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Natural (archaeology); Government (linguistics); Raster data; Aerial photography","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":[0.0004163972,0.001719827,0.001314173,0.004138725,0.001637988,0.003115644,0.00164047,0.0006664295,0.140227],"category_scores_gemma":[0.00261573,0.0009692074,0.0006443667,0.02003037,0.0005332784,0.001061769,0.001056528,0.0009881707,0.1151966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01040055,"about_ca_system_score_gemma":0.017632,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9208487,"about_ca_topic_score_gemma":0.9553222,"domain_scores_codex":[0.9993222,0.00002757274,0.00005259131,0.0001767142,0.0002618375,0.000159229],"domain_scores_gemma":[0.9980881,0.0001172747,0.000170393,0.0002343945,0.001177093,0.0002127466],"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":[0.00002481661,0.000003663418,0.001047434,0.0003283776,0.00001040662,0.00001462447,0.00004770615,0.00007830694,0.00004510895,0.0002329803,0.9950454,0.003121266],"study_design_scores_gemma":[0.00004743985,0.000003168143,0.01227166,0.0001675957,0.00001053245,0.00001660676,0.000134186,0.00006408947,0.00008740323,0.0001701719,0.9870123,0.00001482084],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000820662,0.00005759553,0.00001913115,0.00002284044,0.00001017017,0.000004741587,0.997818,0.00007894383,0.001906515],"genre_scores_gemma":[0.0007389364,0.0001706332,0.0001545968,0.00002221406,0.000005757831,0.00003805177,0.9916838,0.00008341087,0.007102501],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.140227,"threshold_uncertainty_score":0.4691061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01292971790101426,"score_gpt":0.2378293890790131,"score_spread":0.2248996711779989,"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."}}