{"id":"W1994985780","doi":"10.1139/x02-052","title":"Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Vegetation (pathology); Environmental science; Mediterranean climate; Understory; Vegetation type; Satellite; Digital elevation model; Thematic Mapper; Spectral signature; Elevation (ballistics); Fire regime; Satellite imagery; Cartography; Geography; Ecosystem; Ecology","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":[0.0008215004,0.00004486523,0.00009959735,0.0001495665,0.00007141406,0.00004256633,0.0002826836,0.00004297935,0.0003265752],"category_scores_gemma":[0.0001659389,0.00003874644,0.0000105139,0.0002452759,0.0001579519,0.0001558611,0.00003991786,0.0001997274,0.00003113722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001172687,"about_ca_system_score_gemma":0.00008472532,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05792094,"about_ca_topic_score_gemma":0.5475683,"domain_scores_codex":[0.9991097,0.0001000893,0.0002022035,0.0001223973,0.0002800833,0.000185551],"domain_scores_gemma":[0.9992837,0.00006828125,0.00005598259,0.0002823357,0.00004400473,0.0002657155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001290392,0.00003173096,0.7947055,0.00002363511,0.00001919823,0.0002347907,0.002097768,0.0003755985,0.009806256,0.00002343839,0.1645059,0.02816339],"study_design_scores_gemma":[0.0007939066,0.0002396284,0.8284211,0.000151862,0.00001303394,0.0002393443,0.0005693566,0.03181455,0.0007841002,0.001183561,0.13559,0.0001995579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947197,0.001220613,0.00001687896,0.0009160754,0.00007883273,0.0000727754,0.00006800436,8.79583e-7,0.002906227],"genre_scores_gemma":[0.9989979,0.0002319973,0.0004260274,0.00001406249,0.0001641203,1.551616e-7,0.00003689763,0.000005832471,0.0001230618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4896473,"threshold_uncertainty_score":0.9483525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1516649249693391,"score_gpt":0.3161318478071191,"score_spread":0.16446692283778,"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."}}