{"id":"W7017715502","doi":"","title":"Boralex adds 52 MW to its Canadian portfolio - Commodities (COMMODIT) News","year":2015,"lang":"en","type":"other","venue":"","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Portfolio; Production (economics); Government (linguistics); Investment (military)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00150701,0.0009988144,0.0004849098,0.002005258,0.003376314,0.007015778,0.001155273,0.002868251,0.3705209],"category_scores_gemma":[0.003920365,0.0004076475,0.0008169498,0.001203983,0.00118913,0.001649466,0.001747161,0.004456589,0.1476719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008220533,"about_ca_system_score_gemma":0.01763233,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4012061,"about_ca_topic_score_gemma":0.7163518,"domain_scores_codex":[0.9982089,0.00004661426,0.00001921911,0.0000840693,0.001322277,0.0003188637],"domain_scores_gemma":[0.9960731,0.0001801779,0.00004954728,0.0001583647,0.002124267,0.001414594],"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":[0.00001269295,0.000006676006,0.00003152405,0.00001049013,8.427756e-7,0.00001951632,0.000009623282,0.00001551518,0.00007028686,0.001596136,0.9865903,0.01163636],"study_design_scores_gemma":[0.000003792276,0.000003432154,0.0001263025,0.00001426489,0.00000104805,0.000008233012,0.00002458118,0.00002945508,0.00005138052,0.0003230865,0.9994106,0.000003837571],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0007993731,0.002100582,0.001337757,0.03684592,0.02563536,0.0001488654,0.004942684,0.002783742,0.9254057],"genre_scores_gemma":[0.00174357,0.0005894207,0.0005289961,0.002728082,0.0008796928,0.00001315555,0.00102998,0.0003642168,0.9921229],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5987939,"threshold_uncertainty_score":0.8978755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488193050668043,"score_gpt":0.2713075673370077,"score_spread":0.2564256368303273,"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."}}