{"id":"W7098331262","doi":"","title":"the 130 000 km2 Military Training Area of Labrador","year":2006,"lang":"en","type":"article","venue":"","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Training (meteorology); Work (physics); Data collection; Government (linguistics)","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":[0.0001002438,0.000236584,0.0001824457,0.0005279905,0.0004432151,0.0004647273,0.0003766239,0.0002023072,0.001331504],"category_scores_gemma":[0.0002091091,0.0001060923,0.0002077923,0.0005632889,0.0003851283,0.0001671007,0.0004242183,0.0001400494,0.0002403175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188866,"about_ca_system_score_gemma":0.0007225492,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2031554,"about_ca_topic_score_gemma":0.3804126,"domain_scores_codex":[0.9998467,0.00003075255,0.00000503062,0.00003968756,0.00001459637,0.00006326711],"domain_scores_gemma":[0.9997926,0.00002850166,0.00007507692,0.00001353113,0.00002371784,0.00006654015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008566106,0.0002810004,0.9701803,0.0000609561,0.000137663,0.0003885922,0.001218484,0.002903423,0.008371009,0.000137856,0.0007104945,0.01475354],"study_design_scores_gemma":[0.000006049536,0.00008609681,0.9978382,0.000004112909,0.00001345645,0.00003256169,0.0006772103,0.0004835092,0.0003246611,0.000008110876,0.0005227654,0.000003376051],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.9990231,0.00002165281,0.00004039937,0.000006106878,7.593246e-7,0.000005998363,0.0005415652,0.000004946793,0.0003555455],"genre_scores_gemma":[0.9982195,0.0000365688,0.0001445527,0.00001333799,0.00000202261,0.00001444353,0.0007594668,0.000002438247,0.0008076579],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7968446,"threshold_uncertainty_score":0.4039457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04610732845402491,"score_gpt":0.1848962473593277,"score_spread":0.1387889189053028,"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."}}