{"id":"W6969259361","doi":"10.5683/sp3/tfqgpt","title":"Big Island (West) Ontario. 1:50,000. Map Sheet 052E02, ed. 2, 1966","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"General partnership; Georeference; Raster graphics; Natural (archaeology); Government (linguistics); Aerial photography; Geographic information system; Topographic map (neuroanatomy)","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":[0.0003526799,0.00190829,0.001182901,0.004361386,0.001611211,0.003322134,0.00156765,0.0005652916,0.1344046],"category_scores_gemma":[0.002222616,0.0009657601,0.0006999116,0.01807586,0.0005436518,0.001243353,0.00108394,0.001002084,0.1140469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008885194,"about_ca_system_score_gemma":0.01543635,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9065011,"about_ca_topic_score_gemma":0.9516636,"domain_scores_codex":[0.9993708,0.00002263089,0.00004587109,0.0001589047,0.0002543369,0.0001475338],"domain_scores_gemma":[0.9983065,0.00008378768,0.0001565827,0.0001983477,0.001050536,0.0002042715],"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":[0.00001845311,0.00000332998,0.0007848361,0.0003087012,0.00000914177,0.00001266036,0.00004746405,0.00005476302,0.00004774857,0.000195516,0.9957337,0.00278364],"study_design_scores_gemma":[0.00002855242,0.000002493581,0.01074773,0.0001304447,0.000008883608,0.00002003849,0.0001303213,0.00005225949,0.00008413619,0.0001272934,0.9886543,0.00001348852],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008458496,0.0000750195,0.00002367567,0.00002792894,0.00001698022,0.00000631467,0.9970197,0.0001047787,0.002640975],"genre_scores_gemma":[0.0007993721,0.0001915381,0.0002243317,0.00002730655,0.000008095847,0.00004011676,0.9907579,0.0001240189,0.007827247],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1344046,"threshold_uncertainty_score":0.4496282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02006005610095372,"score_gpt":0.2546662985244395,"score_spread":0.2346062424234858,"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."}}