{"id":"W224862179","doi":"","title":"Principle, method and application of FORECAST model.","year":2009,"lang":"en","type":"article","venue":"Zhejiang Linxueyuan xuebao","topic":"Forest, Soil, and Plant Ecology in China","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Process (computing); Forest ecology; Variety (cybernetics); Forest management; Computer science; Ecosystem model; Function (biology); Environmental resource management; Management science; Government (linguistics); Ecosystem management; Mathematical model; Ecosystem; Ecology; Environmental science; Economics; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087068,0.0001087849,0.0002073815,0.00008526132,0.0003011051,0.00002774569,0.0002122934,0.0001861067,0.00001776564],"category_scores_gemma":[0.0001259397,0.0001058316,0.00005410416,0.0002030222,0.0001734156,0.0001686568,0.00002410122,0.0001388134,0.00001593523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000341292,"about_ca_system_score_gemma":0.00008978225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004646692,"about_ca_topic_score_gemma":0.001557771,"domain_scores_codex":[0.9989478,0.0001030845,0.0002291103,0.0002397176,0.00020045,0.0002798646],"domain_scores_gemma":[0.9993197,0.0001666439,0.0001359405,0.000190497,0.0000693617,0.0001178022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006681732,0.0001514017,0.01826588,0.00003559858,0.00003100744,0.00000476151,0.01033532,0.002296129,0.0004631777,0.8732818,0.002241068,0.09282707],"study_design_scores_gemma":[0.001453576,0.0005395297,0.08788921,0.00008221264,0.0001718137,0.00002741105,0.001499642,0.2542833,0.001735994,0.4946475,0.1566732,0.0009966283],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6210539,0.0005815304,0.1111683,0.004877057,0.0003430697,0.001065499,0.00007486504,0.0002822209,0.2605535],"genre_scores_gemma":[0.9791186,0.0001162683,0.018366,0.0003136452,0.0001770515,0.00001159196,0.00001651196,0.00000818042,0.001872143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3786343,"threshold_uncertainty_score":0.4315687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02137803427126157,"score_gpt":0.356942755772488,"score_spread":0.3355647215012264,"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."}}