{"id":"W6950583034","doi":"10.5683/sp3/suijjg","title":"Lake Timagami (East) Ontario. 1:50,000. Map Sheet 041I16, ed. 1, 1957","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Natural (archaeology); Raster graphics; Aerial photography; Topographic map (neuroanatomy); Viewshed analysis; Digital mapping","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":[0.0003347069,0.001767957,0.001149182,0.004360131,0.001624969,0.002668169,0.001639544,0.0005496256,0.1158387],"category_scores_gemma":[0.001946117,0.0008875334,0.0006562093,0.01676395,0.0005247264,0.001047658,0.001080138,0.0008867354,0.08181393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009685965,"about_ca_system_score_gemma":0.01610337,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9192123,"about_ca_topic_score_gemma":0.9640734,"domain_scores_codex":[0.9994646,0.00002091513,0.0000385956,0.0001268372,0.0002085597,0.0001405916],"domain_scores_gemma":[0.9985964,0.00006855525,0.0001441788,0.0001542405,0.0008777014,0.0001589485],"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":[0.00002072762,0.000003715099,0.001178218,0.0003428043,0.0000115814,0.00001561294,0.00006112281,0.00006935984,0.00004827905,0.0002651906,0.9948121,0.00317127],"study_design_scores_gemma":[0.00003714879,0.000002928582,0.01401479,0.0001484078,0.00001111182,0.00002134839,0.0001720689,0.00006872245,0.00008945088,0.0001415421,0.9852784,0.00001413367],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001237818,0.0000765879,0.00002276969,0.0000328145,0.00001340508,0.000006478459,0.9973793,0.00008680748,0.002257933],"genre_scores_gemma":[0.001045619,0.0001840253,0.0001978766,0.00002578828,0.000006980586,0.00004383431,0.9919055,0.00009266882,0.006497754],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1158387,"threshold_uncertainty_score":0.3875191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171021375451089,"score_gpt":0.250660023492364,"score_spread":0.2335578859472551,"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."}}