{"id":"W6969315599","doi":"10.5683/sp3/e9zfjn","title":"Rivière Tortueuse Quebec. 1:50,000. Map Sheet 022N04, ed. 1, 1967","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Natural (archaeology); Digital mapping; Aerial photography; Geographic information system; Government (linguistics); Orthophoto","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":[0.0005447333,0.002111551,0.001467644,0.005870518,0.001514646,0.003087867,0.002054566,0.0007672342,0.1431985],"category_scores_gemma":[0.003337424,0.0008140094,0.0007521496,0.02325976,0.0004518957,0.001127724,0.0008172515,0.001363898,0.09136827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0119254,"about_ca_system_score_gemma":0.01683699,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9653565,"about_ca_topic_score_gemma":0.9741098,"domain_scores_codex":[0.999278,0.00003992419,0.0000470439,0.0001718562,0.0002723592,0.000190816],"domain_scores_gemma":[0.9976742,0.0001424525,0.000133463,0.0002525761,0.001600905,0.0001963839],"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":[0.00001670228,0.000005060373,0.0007733808,0.0001471172,0.000009337466,0.00001086805,0.00001752795,0.0001047355,0.00002731592,0.0002348145,0.9955113,0.003141816],"study_design_scores_gemma":[0.00004881494,0.000004140048,0.01549799,0.0002368925,0.00001176781,0.0000235633,0.0001081521,0.0002229235,0.0001003434,0.0002265148,0.983497,0.00002187835],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008828372,0.00007464221,0.00002601095,0.00003230447,0.0000175789,0.000005586598,0.9979483,0.0001013073,0.00170584],"genre_scores_gemma":[0.0009140861,0.0001678269,0.0001859786,0.00002799613,0.000007461831,0.00003662369,0.991451,0.00009112122,0.007117845],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1431985,"threshold_uncertainty_score":0.4790468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01381117666809661,"score_gpt":0.2299319232565354,"score_spread":0.2161207465884388,"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."}}