{"id":"W6921020667","doi":"10.6084/m9.figshare.28680863.v1","title":"<b>Machine learning-enhanced monitoring of global copper mining areas</b>,","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normalized Difference Vegetation Index; Vegetation (pathology); Tailings; Index (typography); Spatial analysis; Multispectral image; Digital elevation model; Sustainability; Satellite imagery; Land use","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":[0.0005147448,0.0007873485,0.0004827234,0.002166752,0.0003679326,0.0008280168,0.001087486,0.0009909302,0.005336347],"category_scores_gemma":[0.001810668,0.0001961782,0.000649644,0.003103477,0.0002417635,0.000648085,0.000905752,0.000644749,0.004443582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008372399,"about_ca_system_score_gemma":0.001069882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03165053,"about_ca_topic_score_gemma":0.05972698,"domain_scores_codex":[0.9995493,0.00005563286,0.00004621002,0.0001239719,0.0001410443,0.00008378757],"domain_scores_gemma":[0.9989605,0.0001372731,0.0001405121,0.0001919423,0.0004547413,0.0001149283],"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":[0.0003035531,0.0002247541,0.06162562,0.0009823616,0.0001970446,0.0003002936,0.0001139181,0.01258797,0.002610622,0.001083673,0.8783603,0.04160988],"study_design_scores_gemma":[0.0002500454,0.0001737603,0.192566,0.0003540681,0.00009899997,0.0003177806,0.0004657616,0.0333569,0.005066014,0.001554684,0.765706,0.00008999173],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01852611,0.0002109795,0.0009715904,0.0003015103,0.0001102183,0.00006809555,0.975893,0.001021621,0.002896889],"genre_scores_gemma":[0.0180147,0.00008776279,0.002552875,0.00007079383,0.00002704183,0.00009583879,0.9779702,0.00005227233,0.001128624],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03165053,"threshold_uncertainty_score":0.06293261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05148792830957633,"score_gpt":0.2487495631407102,"score_spread":0.1972616348311338,"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."}}