{"id":"W7008456968","doi":"","title":"Canada Injects $13.5 Million into Clean Energy and Forest Industry Transformation in British Columbia","year":2024,"lang":"en","type":"other","venue":"","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Clean energy; Forest industry; Energy (signal processing); Clean technology; Renewable energy; Energy consumption; Transformation (genetics)","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":[0.0005905933,0.0003761958,0.0002877201,0.001201066,0.005521964,0.004725751,0.001171886,0.002850445,0.05377065],"category_scores_gemma":[0.00222677,0.0003591681,0.0004079124,0.002006137,0.001263901,0.0007416122,0.002101861,0.001987252,0.004557292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05337901,"about_ca_system_score_gemma":0.1570687,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.99509,"about_ca_topic_score_gemma":0.9983422,"domain_scores_codex":[0.9986203,0.00006041937,0.00002340495,0.00004983041,0.0005794882,0.0006664986],"domain_scores_gemma":[0.9980034,0.0001262663,0.00004242363,0.00004366478,0.0009672734,0.0008169555],"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":[0.00009577587,0.0001103371,0.008938163,0.0001746869,0.00002480441,0.0002189306,0.0002551647,0.0007512126,0.0005488452,0.01300914,0.908701,0.06717181],"study_design_scores_gemma":[0.00007893256,0.00005393575,0.04791316,0.0003106284,0.00003845343,0.00006955995,0.002374066,0.001977457,0.0009191447,0.002010972,0.9441885,0.00006518632],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.06521823,0.005031115,0.0009266529,0.08897407,0.001576655,0.0002995802,0.01966866,0.0008145429,0.8174905],"genre_scores_gemma":[0.1284394,0.004197054,0.000687131,0.01333605,0.0001216431,0.00009376676,0.00508819,0.0001874364,0.8478494],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.05377065,"threshold_uncertainty_score":0.3872937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006013467187065665,"score_gpt":0.2195922993123041,"score_spread":0.2135788321252384,"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."}}