{"id":"W4396730740","doi":"10.14509/31157","title":"Yukon-Tanana Upland airborne magnetic geophysical data merge, version 2023","year":2024,"lang":"en","type":"report","venue":"","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Merge (version control); Geology; Environmental science; Remote sensing; Geophysics; Physical geography; Geography; Computer science; Information retrieval","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":[0.0007759506,0.001116442,0.0007694267,0.002128789,0.001677671,0.001805996,0.00120614,0.0006857236,0.02478761],"category_scores_gemma":[0.001692742,0.000685975,0.0006508274,0.004585494,0.0002878049,0.001621984,0.001601814,0.000590265,0.01973348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001720425,"about_ca_system_score_gemma":0.01313504,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.469939,"about_ca_topic_score_gemma":0.5768242,"domain_scores_codex":[0.9994475,0.00002525245,0.00004978293,0.0001259844,0.0001790179,0.0001722986],"domain_scores_gemma":[0.9976189,0.00005528806,0.000102027,0.0004727273,0.001572533,0.0001785047],"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":[0.001425205,0.0002888295,0.04599459,0.0003817716,0.0003310157,0.0002760036,0.0005143255,0.002259002,0.0096885,0.002385893,0.8206372,0.1158177],"study_design_scores_gemma":[0.000708302,0.0001073237,0.2227188,0.0001082432,0.0004096139,0.0001412945,0.001212205,0.008569654,0.01468198,0.002124443,0.7490867,0.0001314426],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03267312,0.0001531413,0.00551569,0.0003413911,0.0003409918,0.0003153059,0.9236585,0.006348523,0.0306533],"genre_scores_gemma":[0.02822492,0.00007740142,0.01323906,0.00009093003,0.00002606909,0.0002792958,0.9397085,0.0008396222,0.01751418],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.530061,"threshold_uncertainty_score":0.9344072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04027865832169004,"score_gpt":0.274169784234977,"score_spread":0.2338911259132869,"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."}}