{"id":"W1992869655","doi":"10.5558/tfc76929-6","title":"Spatial implementation of models in forestry","year":2000,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Forest management; Computer science; Process (computing); Forest inventory; Resource (disambiguation); Environmental resource management; Geographic information system; Biomass (ecology); Remote sensing; Spatial analysis; Environmental science; Ecology; Geography; Agroforestry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001090813,0.00005695486,0.00006043558,0.000008800671,0.00005941262,0.000007271979,0.0001555046,0.00002809262,0.002101911],"category_scores_gemma":[0.000001215148,0.00004326205,0.00002788502,0.000131099,0.0001165198,0.000089711,0.00003270021,0.0000708194,0.000137761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007767063,"about_ca_system_score_gemma":0.00001724648,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01278716,"about_ca_topic_score_gemma":0.00326407,"domain_scores_codex":[0.9994135,0.00002290907,0.0001561654,0.0001169339,0.0001343754,0.0001561211],"domain_scores_gemma":[0.9996712,0.00001687376,0.00003688881,0.0002487825,0.000002009846,0.0000243014],"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":[0.00004229947,0.0001300871,0.05683384,0.000009841488,0.00001043784,0.000002206859,0.002240781,0.2560386,0.005286837,0.001228156,0.001958905,0.676218],"study_design_scores_gemma":[0.0009851418,0.00008679797,0.7996271,0.00001461426,0.00001373694,0.00001200838,0.0004279306,0.1549686,0.0137332,0.02652156,0.003430354,0.0001789488],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855025,0.00001733295,0.0004028296,0.0002721943,0.00001140708,0.0001549629,0.000004421854,0.0000135269,0.0136208],"genre_scores_gemma":[0.9994332,0.00001408962,0.0002371207,0.00004266408,0.00002308063,0.000004052971,0.000006074657,0.000006830745,0.0002329459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7427933,"threshold_uncertainty_score":0.9988103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01245029850992931,"score_gpt":0.2608178553638288,"score_spread":0.2483675568538995,"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."}}