{"id":"W2724215599","doi":"10.1080/00076791.2017.1338688","title":"Uniting business history and global environmental history","year":2017,"lang":"en","type":"article","venue":"Business History","topic":"American Environmental and Regional History","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University","funders":"McMaster University","keywords":"Business history; Relevance (law); Theme (computing); Task (project management); Environmental history; Term (time); Political science; Environmental ethics; Sociology; History; Economics; Management; Computer science; Law; Economic history","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":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001938112,0.0004731265,0.000409624,0.00005980044,0.0005680573,0.00001033004,0.0007625275,0.0001786071,0.008694745],"category_scores_gemma":[0.00005527195,0.0004955822,0.0001005205,0.00004727077,0.0114417,0.0008119314,0.0008172783,0.0002315997,0.0009080405],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0278475,"about_ca_system_score_gemma":0.0001650582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003921792,"about_ca_topic_score_gemma":0.0001573809,"domain_scores_codex":[0.9975313,0.00007221387,0.000358482,0.0008762624,0.0006260105,0.0005357335],"domain_scores_gemma":[0.9981154,0.00002658952,0.0005606101,0.0009700262,0.00001092096,0.000316468],"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":[0.0002826151,0.001059756,0.1738153,0.0001665533,0.00008615301,0.0006428593,0.003051839,0.0002797447,0.01850325,0.0006591388,0.7465901,0.05486261],"study_design_scores_gemma":[0.0003321681,0.00001493449,0.4258845,0.00001848369,0.00003028892,0.00006845381,0.0001002693,0.00003808073,0.000002505905,0.00002357789,0.5731061,0.000380646],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6578059,0.02960734,0.0003720374,0.0008014308,0.00573866,0.000481133,0.00003566673,0.0002377996,0.3049201],"genre_scores_gemma":[0.94718,0.0009392107,0.0007591156,0.001751712,0.0003283731,0.00004813605,0.00004104995,0.00008656232,0.04886583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2893742,"threshold_uncertainty_score":0.9998699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615546513614282,"score_gpt":0.1811403673508885,"score_spread":0.1649849022147457,"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."}}