{"id":"W6912991022","doi":"10.5683/sp3/yldikd","title":"Lac Noré Quebec. 1:50,000. Map Sheet 023B04, ed. 1, 1960","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Natural (archaeology); Aerial photography; Geographic information system; Digital mapping; 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":[0.0006264449,0.002362946,0.001591355,0.006262094,0.001718836,0.003692379,0.002712179,0.0009346077,0.1812713],"category_scores_gemma":[0.003980653,0.0008985719,0.0008901737,0.022414,0.0005084404,0.001486566,0.001132795,0.001593354,0.1330166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.010934,"about_ca_system_score_gemma":0.01738826,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.941264,"about_ca_topic_score_gemma":0.9613236,"domain_scores_codex":[0.9991907,0.00004844273,0.00005200121,0.0001955292,0.0002913866,0.0002218506],"domain_scores_gemma":[0.9972399,0.0001838071,0.0001629404,0.0003323701,0.001829173,0.0002517021],"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":[0.00001150027,0.00000341713,0.0004278058,0.0001371489,0.000007133521,0.000007593796,0.00001426582,0.0000590307,0.00002068677,0.0001866056,0.9970428,0.002081956],"study_design_scores_gemma":[0.0000372403,0.000003372522,0.007416819,0.0002396607,0.00001045853,0.00002022456,0.00008959874,0.0001566487,0.00008771889,0.0002627112,0.9916524,0.00002301143],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0000454951,0.00005023047,0.00002306237,0.00002547659,0.00001423727,0.000004730956,0.9981781,0.0001230084,0.001535589],"genre_scores_gemma":[0.0004998598,0.0001057944,0.0001788158,0.00002844444,0.000005866501,0.00003613918,0.9947289,0.0001186787,0.004297527],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1812713,"threshold_uncertainty_score":0.6064128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584992556539143,"score_gpt":0.2644738312310099,"score_spread":0.2486239056656184,"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."}}