{"id":"W6991858007","doi":"","title":"Integrating climate change adaptation and mitigation objectives in British Columbia's forests","year":2019,"lang":"en","type":"article","venue":"Agritrop (Cirad)","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Climate change; Adaptation (eye); Government (linguistics); Forest management; Psychological intervention; Climate change mitigation; Climate change adaptation; Adaptive management; Sustainable forest management","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":[0.003049539,0.0002524191,0.0002061658,0.001762494,0.00873459,0.004645308,0.0009485554,0.0007471962,0.002148645],"category_scores_gemma":[0.005157616,0.000206995,0.0001851869,0.003550604,0.001836086,0.0009002523,0.001801061,0.001147335,0.000108501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1008631,"about_ca_system_score_gemma":0.1458825,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9878854,"about_ca_topic_score_gemma":0.9977481,"domain_scores_codex":[0.9976894,0.0006709488,0.0001095101,0.0002166555,0.0006282965,0.0006852079],"domain_scores_gemma":[0.9956818,0.001341461,0.0002265567,0.0001205172,0.001890321,0.0007392778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000221644,0.0004779646,0.2053121,0.002470035,0.0002303224,0.00296684,0.05751013,0.01356029,0.008275818,0.04375445,0.04288982,0.6223306],"study_design_scores_gemma":[0.0000362341,0.0001108032,0.5550534,0.002050586,0.0002290863,0.0002750887,0.102414,0.004188572,0.002599412,0.006921361,0.3258856,0.0002359752],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7690448,0.01622023,0.003191737,0.02190142,0.0001803365,0.000589068,0.0009503636,0.00009936283,0.1878227],"genre_scores_gemma":[0.9769024,0.003713303,0.004561088,0.001796061,0.00001246632,0.0001244015,0.0002081777,0.00001548951,0.01266666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1008631,"threshold_uncertainty_score":0.7318166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009774631325541266,"score_gpt":0.2090307491595787,"score_spread":0.1992561178340374,"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."}}