{"id":"W6894246387","doi":"10.5683/sp3/d0ghpq","title":"Dryden Ontario. 1:50,000. Map Sheet 052F15, ed. 3, 1988","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; Raster graphics; General partnership; Natural (archaeology); Digital mapping; Government (linguistics); Geographic information system; 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":[0.0004458803,0.001841026,0.001404752,0.005116802,0.001330245,0.002819695,0.001733035,0.0005563394,0.1461216],"category_scores_gemma":[0.002652708,0.001076673,0.0006027487,0.02002334,0.0004243093,0.001150698,0.001047589,0.0008909803,0.1293346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007803777,"about_ca_system_score_gemma":0.01161851,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8233138,"about_ca_topic_score_gemma":0.8982624,"domain_scores_codex":[0.9993494,0.00002871913,0.00005447189,0.0001562302,0.0002772877,0.0001338859],"domain_scores_gemma":[0.9983037,0.000114759,0.0001761992,0.0002396725,0.0009853625,0.0001802921],"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":[0.00001417044,0.000003184291,0.0005979089,0.0002417892,0.000006444362,0.000009688573,0.00003307832,0.00005358308,0.00003763287,0.0001714819,0.9956046,0.003226349],"study_design_scores_gemma":[0.00002408106,0.000002153052,0.008255022,0.000110873,0.000006907917,0.00001513995,0.00008345053,0.00005503228,0.00007735154,0.0001350061,0.9912247,0.00001027193],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005872525,0.00004552682,0.00002416526,0.00001999019,0.000008460473,0.00000447188,0.9979248,0.0001017974,0.001812076],"genre_scores_gemma":[0.0004009292,0.0001273407,0.0001925254,0.00001635486,0.000004371771,0.00004102704,0.9936357,0.00009463837,0.005487232],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1766862,"threshold_uncertainty_score":0.4888256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01733082114734629,"score_gpt":0.2587124554981693,"score_spread":0.241381634350823,"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."}}