{"id":"W6950131705","doi":"10.5683/sp3/fpzebg","title":"Lac Wiashgamic Quebec. 1:50,000. Map Sheet 032C10, ed. 1, 1967","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":[0.0005544241,0.002229748,0.001451995,0.005680847,0.001773028,0.003429936,0.00238145,0.0007185593,0.1876038],"category_scores_gemma":[0.003165239,0.0008947154,0.0007171637,0.02560173,0.0004933054,0.001233926,0.0009642108,0.001388945,0.1127124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01434214,"about_ca_system_score_gemma":0.02144423,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9719963,"about_ca_topic_score_gemma":0.9823146,"domain_scores_codex":[0.9992384,0.00003852979,0.00004129191,0.0001770252,0.0002964253,0.0002083404],"domain_scores_gemma":[0.9975283,0.0001315006,0.0001420518,0.0002821817,0.00168003,0.0002359133],"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":[0.00001748334,0.000004496803,0.0006651516,0.0001621491,0.000008352818,0.00001094937,0.00002611559,0.00008992002,0.00003177357,0.0002521465,0.9947719,0.003959575],"study_design_scores_gemma":[0.00003549492,0.000003695032,0.01129116,0.0002088332,0.000009041659,0.00002152024,0.0001308802,0.0001590344,0.00009539013,0.0002021076,0.9878207,0.00002223657],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008054893,0.00006907248,0.00003647839,0.00002966357,0.00001673789,0.000007256666,0.9967643,0.0001388942,0.002857058],"genre_scores_gemma":[0.001155886,0.0001858651,0.0002939203,0.00003281181,0.000007348409,0.00005587883,0.9879512,0.0001632817,0.01015368],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1876038,"threshold_uncertainty_score":0.6275973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01469286592777449,"score_gpt":0.2621925977674181,"score_spread":0.2474997318396436,"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."}}