{"id":"W3166983810","doi":"","title":"Climate change mitigation in British Columbia's forest sector: biophysical impacts and costs","year":2016,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Climate change; Natural resource economics; Environmental resource management; Environmental science; Environmental protection; Geography; Economics; Ecology","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":[0.0008175537,0.0003669649,0.000266277,0.001209829,0.002315756,0.003555412,0.0007512033,0.0009855955,0.007648581],"category_scores_gemma":[0.003473523,0.0002964524,0.0003321657,0.002872586,0.0008428897,0.0008544576,0.001072219,0.001160635,0.0003733791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03198017,"about_ca_system_score_gemma":0.04228769,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9697005,"about_ca_topic_score_gemma":0.9920244,"domain_scores_codex":[0.9988338,0.0002288307,0.00003452983,0.00005558249,0.0003475036,0.0004997253],"domain_scores_gemma":[0.9982868,0.0002959705,0.0001073941,0.00005286743,0.0008375156,0.0004194847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001117224,0.0004295422,0.4195965,0.0006910547,0.000587128,0.001786565,0.001833746,0.05405265,0.003552924,0.02700788,0.1306892,0.3586557],"study_design_scores_gemma":[0.00009660752,0.00008099366,0.841549,0.0005266737,0.0005556034,0.0002637251,0.01179367,0.02200129,0.001778661,0.007119888,0.1141224,0.0001114023],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8158025,0.008064787,0.000408276,0.02747545,0.0002298173,0.00009001253,0.005096457,0.00007013058,0.1427627],"genre_scores_gemma":[0.9678964,0.004627593,0.0003299036,0.0008723761,0.00003765731,0.00002111812,0.0008067022,0.00002206454,0.02538618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03198017,"threshold_uncertainty_score":0.2320335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0115871505991627,"score_gpt":0.2238647717696067,"score_spread":0.212277621170444,"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."}}