{"id":"W7038290631","doi":"","title":"Hydro-Québec Power Helps New England Meet Its GHG Reduction Goals","year":2016,"lang":"en","type":"other","venue":"","topic":"Coleoptera: Cerambycidae studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"New england; Reduction (mathematics); Power (physics); Greenhouse gas; Production (economics)","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":[0.0004500579,0.0003262298,0.0001518434,0.0006717017,0.001758564,0.001532632,0.0005081362,0.000589497,0.05674539],"category_scores_gemma":[0.0009528565,0.0001175979,0.0001830209,0.0009171763,0.0004259327,0.0006901963,0.0005926416,0.0005849074,0.003038524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01395162,"about_ca_system_score_gemma":0.02067724,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9692294,"about_ca_topic_score_gemma":0.9940584,"domain_scores_codex":[0.9997403,0.00002359971,0.000003335125,0.00002253083,0.0001368855,0.00007327947],"domain_scores_gemma":[0.9992637,0.00005426558,0.0000366235,0.00003041322,0.0004497404,0.0001653848],"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":[0.0003633638,0.0001445406,0.05031097,0.0003986226,0.00008777821,0.000404153,0.000896063,0.001387372,0.004073909,0.01338813,0.6865275,0.2420176],"study_design_scores_gemma":[0.00004253761,0.00005122499,0.08980853,0.0001448278,0.0000259162,0.00007744943,0.001303233,0.0008190751,0.001246136,0.00081165,0.9056494,0.00002010596],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.1246331,0.006318098,0.002205754,0.02259162,0.00061788,0.0002962716,0.01513363,0.0005472579,0.8276564],"genre_scores_gemma":[0.2160405,0.00353841,0.003673299,0.003202433,0.000113003,0.00009180293,0.003673809,0.0001736686,0.7694931],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.05674539,"threshold_uncertainty_score":0.1898322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237665216540145,"score_gpt":0.2185094598335302,"score_spread":0.2061328076681288,"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."}}