{"id":"W6913277475","doi":"10.5683/sp3/cugmzn","title":"Ranger Lake Ontario. 1:50,000. Map Sheet 041J13, ed. 3, 1975","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); Orthophoto; 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":[0.0004443617,0.001928182,0.00139491,0.005551533,0.001559923,0.002915753,0.001892464,0.0005866183,0.1446215],"category_scores_gemma":[0.002576953,0.001037967,0.0006368394,0.02249001,0.0004861632,0.00127531,0.00112643,0.0009512451,0.1132632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008274176,"about_ca_system_score_gemma":0.01339629,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8757606,"about_ca_topic_score_gemma":0.9321311,"domain_scores_codex":[0.9992631,0.00003135343,0.00005964448,0.0001740813,0.0003059308,0.000165874],"domain_scores_gemma":[0.9981425,0.0001168555,0.0001910279,0.0002220193,0.001134853,0.0001927558],"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":[0.00001613614,0.000003417325,0.0007168927,0.0003048992,0.000008913423,0.00001274988,0.00004032137,0.00006156923,0.00003995954,0.000215126,0.9955643,0.003015568],"study_design_scores_gemma":[0.00003094624,0.000002889723,0.01060491,0.0001465111,0.00001039315,0.00002039304,0.0001138977,0.00006488415,0.00007496448,0.000159883,0.988755,0.00001535901],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006638747,0.00005679337,0.00002290868,0.00002260709,0.000009236835,0.00000466996,0.9978449,0.00007995443,0.001892453],"genre_scores_gemma":[0.0005416435,0.0001575352,0.0001938581,0.00001890672,0.000005472048,0.00004443403,0.9936162,0.00008463527,0.005337284],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1446215,"threshold_uncertainty_score":0.4838071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01767456352583497,"score_gpt":0.2542341356880827,"score_spread":0.2365595721622477,"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."}}