{"id":"W4399784221","doi":"10.25518/0037-9565.11908","title":"Accessibility of the ILMT Survey Data","year":2024,"lang":"en","type":"article","venue":"Bulletin de la Société Royale des Sciences de Liège","topic":"Migration, Aging, and Tourism Studies","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Service Public de Wallonie; Université de Liège; Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS; Department of Science and Technology, Ministry of Science and Technology, India; York University","keywords":"Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0113115,0.0001186557,0.0001695744,0.00004326193,0.001848961,0.0004471923,0.00191782,0.0001058527,0.0008185426],"category_scores_gemma":[0.001850376,0.00008355714,0.00008826673,0.0007206128,0.005774731,0.0001710738,0.000458008,0.0001875415,0.00002492943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001433514,"about_ca_system_score_gemma":0.0008644972,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05506035,"about_ca_topic_score_gemma":0.02546088,"domain_scores_codex":[0.9967281,0.001567896,0.0002504492,0.0004552041,0.0005565618,0.0004417951],"domain_scores_gemma":[0.9972214,0.002087765,0.000100119,0.0004221095,0.00009527434,0.00007335133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008934953,0.0001086851,0.5201875,0.0001191146,0.00004945739,0.000005688772,0.1908486,0.00004656105,0.00004904723,0.01547043,0.2676059,0.005500022],"study_design_scores_gemma":[0.0001180022,0.00002838607,0.7526451,0.0001028078,0.0000416634,0.000002308012,0.004240897,0.0009418569,0.0001134695,0.01968656,0.2218573,0.0002216169],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9235737,0.005267697,0.0006816464,0.003248799,0.0006913259,0.0002496719,0.0001065765,0.0001390134,0.06604154],"genre_scores_gemma":[0.9934211,0.0004679736,0.001290485,0.000249452,0.0002714373,0.00001029509,0.00000385442,0.000008134764,0.00427722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2324576,"threshold_uncertainty_score":0.9994505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1057439609608864,"score_gpt":0.4214090186594907,"score_spread":0.3156650576986043,"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."}}