{"id":"W6913061394","doi":"10.5683/sp3/ozb24a","title":"Algonquin (East) Ontario. 1:50,000. Map Sheet 031E10, ed. 2, 1960","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; Viewshed analysis; Raster graphics; Natural (archaeology); Aerial photography; Geographic information system; Orthophoto","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":[0.0004766408,0.001892866,0.001284831,0.004290462,0.001371577,0.003426341,0.001710197,0.0005936677,0.171582],"category_scores_gemma":[0.002649876,0.0009511785,0.0007076531,0.01581752,0.0004884246,0.001330753,0.001226121,0.0009595265,0.1454343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007035621,"about_ca_system_score_gemma":0.01156498,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8564873,"about_ca_topic_score_gemma":0.9223551,"domain_scores_codex":[0.9993356,0.0000312424,0.00004799319,0.0001811087,0.000250572,0.0001534725],"domain_scores_gemma":[0.9982927,0.0001183038,0.0001497028,0.0002375477,0.0009966018,0.0002051613],"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":[0.00001604169,0.000002344605,0.000538879,0.0002626254,0.000007661783,0.00001082298,0.00003609032,0.00005486421,0.00004088348,0.0002335019,0.9959671,0.002829158],"study_design_scores_gemma":[0.00002343813,0.000002092443,0.006019947,0.0001270768,0.000006574687,0.000017017,0.00008438768,0.0000491426,0.00006108959,0.0001775063,0.9934203,0.0000113955],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006212556,0.00007997205,0.00003022265,0.00002716357,0.00001443468,0.000004904566,0.9971715,0.0001612635,0.002448551],"genre_scores_gemma":[0.0005805198,0.0001812215,0.0002360366,0.00002673981,0.000007262892,0.00003509234,0.9921544,0.0001900297,0.00658861],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.171582,"threshold_uncertainty_score":0.5739989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889962112330962,"score_gpt":0.2556260818454821,"score_spread":0.2367264607221725,"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."}}