{"id":"W3123062529","doi":"10.1088/2633-1357/abdbbb","title":"A novel dataset for analysing sub-national socioeconomic developments in the Indian coal industry","year":2021,"lang":"en","type":"article","venue":"IOP SciNotes","topic":"Energy and Environment Impacts","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Coal; Coal mining; Dependency (UML); Work (physics); Scale (ratio); Socioeconomic status; Unit (ring theory); Business; Geography; Natural resource economics; Economic growth; Economics; Engineering; Mathematics; Population; Sociology","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004125102,0.00009021407,0.00008320656,0.00002936031,0.0001528218,0.00005193716,0.0001970091,0.0001008447,0.0004120451],"category_scores_gemma":[0.00007296457,0.00007522154,0.00002736796,0.0001091364,0.00008846648,0.0002349207,0.00009925829,0.0001573915,0.0001385096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001869867,"about_ca_system_score_gemma":0.00004711859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001727071,"about_ca_topic_score_gemma":0.001179816,"domain_scores_codex":[0.9991755,0.00002930172,0.0001521227,0.0002217974,0.0001963595,0.0002249253],"domain_scores_gemma":[0.9996246,0.0001390265,0.00005051417,0.0001345572,0.000002315967,0.00004900531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000203191,0.0003748943,0.8445273,0.00001079692,0.00004873285,0.00002774866,0.001973756,0.09682623,0.03558766,0.0002452985,0.01704725,0.003310007],"study_design_scores_gemma":[0.0004110022,0.000008300614,0.9824376,0.000005960047,0.000008505825,0.00001698448,0.0002976151,0.000230288,0.006319097,0.0004205985,0.009703117,0.0001409844],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977353,0.00001044127,0.0001215985,0.001210187,0.00004096813,0.0000679193,0.0002994017,0.000003896329,0.0005102494],"genre_scores_gemma":[0.9951631,0.000003984677,0.001512396,0.002300462,0.00003771485,0.00001595016,0.0008891682,0.000006088849,0.00007117663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1379102,"threshold_uncertainty_score":0.4511605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03659556379550769,"score_gpt":0.2751890689757893,"score_spread":0.2385935051802816,"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."}}