{"id":"W6969136027","doi":"10.5683/sp3/gvrb2p","title":"Red Willow Lake Ontario. 1:50,000. Map Sheet 053D06, ed. 1, 1983","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"Modeling and Simulation Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; Aerial photography; General partnership; Willow; Natural (archaeology); Raster graphics; Aerial photos; Geographic information system; Natural resource","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":[0.0003615326,0.001851338,0.001347523,0.004264098,0.001541855,0.002970884,0.00175431,0.0006061624,0.1361154],"category_scores_gemma":[0.002288068,0.0009481072,0.0005958564,0.02090608,0.0004771529,0.001115158,0.00103073,0.0009122377,0.116839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008851629,"about_ca_system_score_gemma":0.01566464,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9124355,"about_ca_topic_score_gemma":0.950691,"domain_scores_codex":[0.9994479,0.00002081335,0.00004393757,0.0001318074,0.0002221467,0.000133322],"domain_scores_gemma":[0.9982925,0.00008829581,0.0001472926,0.0001969258,0.001092539,0.0001823225],"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":[0.00001533088,0.000003465307,0.0006416881,0.0002279363,0.000006818751,0.00001139023,0.00002955765,0.00006503957,0.00003800058,0.0001655128,0.9957982,0.00299697],"study_design_scores_gemma":[0.0000380955,0.000002875743,0.01073531,0.0001545433,0.00001000828,0.00001866059,0.0001239676,0.0000921371,0.0000997347,0.0001875153,0.9885228,0.00001428745],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007656672,0.00005112443,0.00002297492,0.00002756155,0.00001026086,0.000004914546,0.9979388,0.00009939002,0.001768415],"genre_scores_gemma":[0.0005485601,0.0001812852,0.0001674589,0.00002186914,0.00000521212,0.00003768493,0.9925686,0.00007937435,0.006390076],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9124355,"threshold_uncertainty_score":0.4553512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02320155565686361,"score_gpt":0.2506832846596809,"score_spread":0.2274817290028173,"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."}}