{"id":"W7056102207","doi":"","title":"EverWind to invest $1-billion in renewable energy to power Nova Scotia project","year":2023,"lang":"en","type":"other","venue":"","topic":"Magnetic Field Sensors Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Nova scotia; Renewable energy; Power (physics); Energy (signal processing); Investment (military); Wind power; Work (physics)","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":[0.0005794852,0.0005232492,0.0001918315,0.0008185402,0.002038206,0.002271116,0.0005249695,0.001583564,0.1766588],"category_scores_gemma":[0.001251338,0.0002397975,0.0003981579,0.0002837291,0.0004870101,0.0005714347,0.001401992,0.001419671,0.04177573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005642548,"about_ca_system_score_gemma":0.01620657,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4352534,"about_ca_topic_score_gemma":0.7708205,"domain_scores_codex":[0.999473,0.00002725297,0.000006632299,0.00003597275,0.0002683148,0.0001888144],"domain_scores_gemma":[0.9987495,0.00006656493,0.00002504347,0.000062839,0.0006046203,0.0004915097],"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":[0.0002038082,0.0000941833,0.001928486,0.00007861493,0.00001371092,0.0002928597,0.00006958072,0.0003060912,0.001902095,0.01073927,0.9213133,0.06305793],"study_design_scores_gemma":[0.0000464226,0.00004698891,0.004736661,0.00008588347,0.000006525341,0.00007887976,0.0002228381,0.0004912789,0.0007344342,0.0009303339,0.9926099,0.000009886357],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0120134,0.0008954306,0.001098457,0.01192592,0.002825771,0.0002481766,0.00567179,0.0007445434,0.9645765],"genre_scores_gemma":[0.01192882,0.0002077321,0.0005498537,0.001049856,0.00003853457,0.00002347068,0.0007718473,0.00009277205,0.9853372],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5647466,"threshold_uncertainty_score":0.86544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01405026520871181,"score_gpt":0.2402416647865624,"score_spread":0.2261913995778506,"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."}}