{"id":"W6950567874","doi":"10.5683/sp3/0ghvoa","title":"Windsor Ontario. 1:50,000. Map Sheet 040J06, ed. 5, 1979","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Windsor; Georeference; General partnership; Aerial photography; Natural (archaeology); Orthophoto; Raster graphics; Geographic information system","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":[0.0005149021,0.002253876,0.001436422,0.005108288,0.00131342,0.003231142,0.001743142,0.000687918,0.1928796],"category_scores_gemma":[0.002692447,0.0011678,0.000696165,0.02326688,0.0004338364,0.001551416,0.001024745,0.001063167,0.1919677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006132251,"about_ca_system_score_gemma":0.01070062,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7650591,"about_ca_topic_score_gemma":0.8450091,"domain_scores_codex":[0.9992651,0.00003841078,0.00007695851,0.0001838503,0.0002775739,0.0001580662],"domain_scores_gemma":[0.9981716,0.0001241505,0.0001872689,0.0002406328,0.001081983,0.0001943556],"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":[0.00001158952,0.000002842366,0.0004279035,0.0002080869,0.000006134589,0.000007599468,0.00002074582,0.00004109973,0.00002524127,0.0001480757,0.9969599,0.002140769],"study_design_scores_gemma":[0.00003020255,0.000002410497,0.006266637,0.0001361459,0.000007529597,0.00001456081,0.00008706497,0.0000423383,0.00006569558,0.0001468798,0.9931893,0.00001120727],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000369606,0.00003605266,0.00001703737,0.00001750787,0.00000952069,0.000003350355,0.9983058,0.00006449173,0.001509282],"genre_scores_gemma":[0.0002778677,0.0001282457,0.0001390081,0.00001532523,0.000004869446,0.00003327447,0.9941202,0.00008055606,0.005200659],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2349409,"threshold_uncertainty_score":0.6452466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01770165550511626,"score_gpt":0.2572302800478493,"score_spread":0.239528624542733,"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."}}