{"id":"W6931763934","doi":"10.5683/sp3/ldscy1","title":"Lac Humqui (East) Quebec. 1:50,000. Map Sheet 022B05, ed. 1, 1958","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Natural (archaeology); Aerial photography; Geographic information system; Digital mapping; Viewshed analysis","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007578126,0.0005584489,0.000623874,0.0003621453,0.0005130646,0.0005506533,0.004234117,0.0003097508,0.008241681],"category_scores_gemma":[0.0001273467,0.0005789444,0.000274905,0.0003973322,0.00008667028,0.0002892073,0.00206066,0.00107928,0.0002413916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001981652,"about_ca_system_score_gemma":0.0003263976,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4569455,"about_ca_topic_score_gemma":0.06873471,"domain_scores_codex":[0.9959912,0.0004592661,0.0005227738,0.001176436,0.001052133,0.0007982409],"domain_scores_gemma":[0.996149,0.0001404461,0.0003777688,0.002960521,0.000068697,0.0003035504],"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":[0.00001116327,0.0001115419,0.000007555047,0.0001091054,0.00005532521,0.0001595585,0.0001389719,0.0001592076,3.903421e-7,0.001074486,0.9881061,0.0100666],"study_design_scores_gemma":[0.0002851203,0.0001145558,0.0002398512,0.00004110939,0.00004510876,0.00003980037,0.00002053963,0.0005747482,8.741345e-7,0.00022404,0.9977522,0.0006620627],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000001616639,0.0006476688,0.0009369086,0.003856087,0.001344642,0.0002350072,0.9907554,0.000422845,0.001799839],"genre_scores_gemma":[0.000003550207,0.0001677011,0.000879914,0.002031011,0.0007172856,0.0001134145,0.9921561,0.00004441315,0.003886542],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3882108,"threshold_uncertainty_score":0.9996662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126549488708715,"score_gpt":0.2513912884103065,"score_spread":0.2401257935232193,"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."}}