{"id":"W4406782797","doi":"10.3390/wind5010002","title":"A Wind Offset Paradox: Alberta’s Wind Fleet Displacing Greenhouse Gas Emissions and Depressing Future Offset Values","year":2025,"lang":"en","type":"article","venue":"Wind","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; University of Alberta","keywords":"Offset (computer science); Greenhouse gas; Environmental science; Meteorology; Carbon offset; Atmospheric sciences; Geography; Physics; Geology; Oceanography; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003336053,0.0003169001,0.0006013454,0.0003549329,0.0004706043,0.0002462709,0.0002596796,0.0002771523,0.0003447735],"category_scores_gemma":[0.0001222711,0.0003571888,0.0001407777,0.0002542876,0.0001265198,0.0004083927,0.0002148153,0.000294007,0.000174035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001142184,"about_ca_system_score_gemma":0.00003666061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001036047,"about_ca_topic_score_gemma":0.0006593785,"domain_scores_codex":[0.9980199,0.00002443283,0.000701984,0.0006770457,0.00003562132,0.0005410491],"domain_scores_gemma":[0.998775,0.0001814548,0.0002986413,0.0005300234,0.00001675425,0.000198148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003791903,0.0006586342,0.7594162,0.0007493991,0.0007469281,0.00004365945,0.02047073,0.0009997817,0.0003192087,0.1309713,0.06119774,0.02404729],"study_design_scores_gemma":[0.005071247,0.0002103015,0.1843425,0.0007635934,0.0001885905,0.0001009998,0.003617943,0.02658561,0.0004998986,0.1335124,0.642787,0.002319852],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9693431,0.003204693,0.0000885947,0.004757378,0.0007807558,0.0002587745,0.0005730293,0.00004760519,0.02094608],"genre_scores_gemma":[0.9931325,0.0009882973,0.0003060657,0.001164953,0.0006318155,0.000006574428,0.0001260431,0.0000488787,0.003594832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5815893,"threshold_uncertainty_score":0.999888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03533748739373063,"score_gpt":0.2588293947605442,"score_spread":0.2234919073668136,"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."}}