{"id":"W2315850920","doi":"10.5558/tfc2012-057","title":"Stakeholder identification and analysis for adaptive governance in the Kovdozersky Model Forest, Russian Federation","year":2012,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Interreg; Ministry of Environment; Svenska Forskningsrådet Formas","keywords":"Stakeholder; Biome; Corporate governance; Sustainable forest management; Environmental resource management; Forest management; Business; Taiga; Identification (biology); Order (exchange); Forestry; Environmental planning; Geography; Political science; Ecology; Ecosystem; Economics; Public relations","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005414643,0.00009715173,0.00008123834,0.00002034983,0.000263162,0.00007889853,0.0002426739,0.00003488978,0.0001163262],"category_scores_gemma":[0.00001434875,0.00005992996,0.00005157515,0.0003137868,0.0001142152,0.0004738494,0.00007030345,0.00007458124,0.0000574832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001207017,"about_ca_system_score_gemma":0.000007387104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00050828,"about_ca_topic_score_gemma":0.002005308,"domain_scores_codex":[0.9991731,0.00004246913,0.000153638,0.0001539289,0.0001886689,0.0002882181],"domain_scores_gemma":[0.999529,0.0000545914,0.00009369549,0.0002869912,0.000002594261,0.00003313981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008608271,0.0001706346,0.4803016,0.00001840872,0.0001030405,4.569472e-7,0.005832703,0.3051465,0.0001524099,0.1930078,0.01194169,0.003238761],"study_design_scores_gemma":[0.0002078118,0.00001993192,0.5749231,0.000002098061,0.00007323185,5.643623e-7,0.00008759975,0.4195164,0.00003628484,0.004030936,0.001025342,0.00007673246],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836443,0.0001667353,0.007918241,0.001786051,0.00003358294,0.0006102285,0.00001930038,0.00001555607,0.005806021],"genre_scores_gemma":[0.9976366,0.00002335546,0.0002255528,0.0002427068,0.00006278463,0.0001110884,0.00001961671,0.000008274466,0.001669993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1889769,"threshold_uncertainty_score":0.2443873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03931438928583063,"score_gpt":0.2567873407138541,"score_spread":0.2174729514280235,"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."}}