{"id":"W6950920342","doi":"10.5683/sp3/hxgaey","title":"Dog Harbour Ontario. 1:50,000. Map Sheet 041N14, ed. 2, 1976","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; Raster graphics; Natural (archaeology); Aerial photography; Harbour; Government (linguistics); Geographic information system","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":[0.0004094769,0.001834697,0.001287814,0.00561628,0.001497047,0.002907871,0.001752602,0.0005637078,0.141178],"category_scores_gemma":[0.002621504,0.000971329,0.0006593142,0.0224666,0.0005045109,0.001153228,0.001067579,0.0009038605,0.1114354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009497853,"about_ca_system_score_gemma":0.01709019,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9010764,"about_ca_topic_score_gemma":0.9466365,"domain_scores_codex":[0.9993513,0.00002518357,0.00005430953,0.0001421543,0.0002714089,0.0001556204],"domain_scores_gemma":[0.9981489,0.0001080756,0.0001854047,0.0001964198,0.001142659,0.0002185195],"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":[0.00001639443,0.000003415581,0.0008033221,0.0002891929,0.000008716171,0.00001297431,0.00003902377,0.00006660711,0.00003771871,0.0002107705,0.995789,0.002722904],"study_design_scores_gemma":[0.00003063785,0.000002878593,0.0116494,0.000138421,0.000009938046,0.00001914334,0.0001283413,0.00006432387,0.00007393253,0.0001485453,0.9877204,0.00001399299],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006666136,0.00004829613,0.00001886809,0.00002476736,0.00001154922,0.000004526825,0.9979954,0.00006498087,0.001765047],"genre_scores_gemma":[0.0005862218,0.0001607204,0.0001635828,0.00001854833,0.000006638636,0.00003836749,0.9933102,0.00007663417,0.005639161],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.141178,"threshold_uncertainty_score":0.4722875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01715179746438023,"score_gpt":0.2615767316807845,"score_spread":0.2444249342164043,"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."}}