{"id":"W4241326591","doi":"10.1163/9789004322714_cclc_2017-0220-001","title":"MANAGING YOUR CARBON FOOTPRINT: IMPACTS AND OPPORTUNITIES ARISING FROM ALBERTA’S CLIMATE CHANGE LEGISLATION","year":2018,"lang":"en","type":"dataset","venue":"Climate Change and Law Collection","topic":"Environmental Policies and Emissions","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Carbon footprint; Legislation; Climate change; Footprint; Environmental resource management; Carbon fibers; Natural resource economics; Environmental science; Business; Environmental protection; Geography; Greenhouse gas; Political science; Geology; Computer science; Economics; Oceanography; Archaeology; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001757709,0.0003872628,0.0003319528,0.00009698656,0.0009498814,0.0001715913,0.0001132576,0.0003062475,0.0003514092],"category_scores_gemma":[0.000007624892,0.0003753777,0.00005386721,0.0001179849,0.0003151112,0.0002676021,0.0006173128,0.0002232132,0.00002512091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002357843,"about_ca_system_score_gemma":0.000003181596,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1118964,"about_ca_topic_score_gemma":0.03755798,"domain_scores_codex":[0.9982973,0.00006698945,0.0002845404,0.0005569864,0.0002342706,0.0005599085],"domain_scores_gemma":[0.9991438,0.00004843221,0.000246645,0.0003161759,0.000004851675,0.0002400943],"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":[0.0003240397,0.0004315078,0.008103209,0.001179555,0.000189997,0.0001029113,0.0152434,0.000008737888,0.001741357,0.0002442208,0.9392319,0.03319914],"study_design_scores_gemma":[0.0004527944,0.0002639934,0.009618323,0.0005775527,0.0002304972,0.00003247112,0.0007531441,0.0006922905,0.00009558445,0.0003438595,0.9862573,0.000682246],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1710398,0.002590603,0.000001473469,0.003083434,0.001681719,0.001931034,0.7940605,0.0001298386,0.02548159],"genre_scores_gemma":[0.03484663,0.2502218,0.00006389597,0.002872747,0.002730681,0.0004056903,0.7082021,0.0001337872,0.0005227062],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2476311,"threshold_uncertainty_score":0.9998698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08751798946629888,"score_gpt":0.2738495151344649,"score_spread":0.186331525668166,"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."}}