{"id":"W6950592948","doi":"10.5683/sp3/cte3dm","title":"Welland Ontario. 1:50,000. Map Sheet 030L14, ed. 4, 1973","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; Orthophoto; General partnership; Raster graphics; Natural (archaeology); Aerial photography; Digital mapping; Government (linguistics); Viewshed analysis","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":[0.0004719021,0.001931289,0.001403319,0.005548281,0.001653822,0.003342554,0.00174062,0.0006507796,0.1635128],"category_scores_gemma":[0.003126582,0.001054527,0.0006951698,0.02363146,0.0005592267,0.001343516,0.001179279,0.0009993179,0.1335737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01075725,"about_ca_system_score_gemma":0.01844119,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9011739,"about_ca_topic_score_gemma":0.9450757,"domain_scores_codex":[0.9992341,0.00003386093,0.00006011053,0.000175672,0.000313885,0.0001823386],"domain_scores_gemma":[0.9978611,0.0001518906,0.0002012745,0.0002555206,0.001303606,0.0002266144],"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":[0.00001510943,0.00000313622,0.0006180663,0.0002805997,0.000008228703,0.00001130746,0.00003610416,0.00005956529,0.00003654212,0.0002225352,0.9962115,0.002497409],"study_design_scores_gemma":[0.00002839941,0.00000236185,0.008587093,0.0001509075,0.000009308688,0.0000176157,0.0001059502,0.00006235689,0.00008035936,0.0001896847,0.9907514,0.00001455673],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005359642,0.00005713728,0.00002016995,0.0000251923,0.00000942211,0.000003923156,0.9979785,0.00008404116,0.001768011],"genre_scores_gemma":[0.0005597745,0.0001791671,0.0001832443,0.00002431122,0.000005829775,0.00003701175,0.9929848,0.0001136562,0.005912236],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1635128,"threshold_uncertainty_score":0.5470049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01588363271539205,"score_gpt":0.2513846201417946,"score_spread":0.2355009874264026,"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."}}