{"id":"W2077202991","doi":"10.1016/j.rse.2009.04.008","title":"Evaluation of annual forest disturbance monitoring using a static decision tree approach and 250 m MODIS data","year":2009,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Normalization (sociology); Change detection; Decision tree; Computer science; Cohen's kappa; Kappa; Remote sensing; Data mining; Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003368573,0.0005170696,0.0005799234,0.0009566266,0.0003435455,0.0004619547,0.0006269451,0.000432292,0.0008933694],"category_scores_gemma":[0.005270241,0.0002090857,0.0003418975,0.0008556023,0.0001733696,0.0008283956,0.0002711283,0.0001560847,0.0001199766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007191398,"about_ca_system_score_gemma":0.0007329827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01467097,"about_ca_topic_score_gemma":0.01531274,"domain_scores_codex":[0.999196,0.0002887486,0.00007138662,0.0001438935,0.0002280339,0.00007206807],"domain_scores_gemma":[0.9940243,0.004330645,0.0002948521,0.0002064925,0.000895194,0.0002484727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.008484134,0.001843818,0.2018664,0.0003561766,0.0005622995,0.0004330713,0.0002378441,0.4984376,0.0190364,0.0006432927,0.001190092,0.2669088],"study_design_scores_gemma":[0.0001189502,0.001298494,0.06126394,0.000009442515,0.0001637402,0.00007520475,0.00009715572,0.9331711,0.003388669,0.0001895274,0.00020771,0.00001610579],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911386,0.00011831,0.007551957,0.00002759478,0.00001361873,0.00003208624,0.0002254477,0.0001422235,0.0007501006],"genre_scores_gemma":[0.9943323,0.00003499313,0.005232299,0.000007657103,0.000005126656,0.00001160026,0.0001976033,0.000007147355,0.0001712472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01467097,"threshold_uncertainty_score":0.02917117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04905721897646079,"score_gpt":0.2822607351625055,"score_spread":0.2332035161860447,"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."}}