{"id":"W2070624879","doi":"10.1016/j.rse.2008.01.010","title":"Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":114,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; University of British Columbia; Canadian Forest Service","funders":"Natural Resources Canada","keywords":"Panchromatic film; Remote sensing; Population; Environmental science; Satellite; Tree (set theory); Geography; Mathematics; Multispectral image; Physics","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":[0.0002650041,0.0002900591,0.0002178606,0.002377676,0.0001926258,0.0004107714,0.0002217529,0.0002536101,0.001561305],"category_scores_gemma":[0.0004704195,0.0001491604,0.0004222977,0.001894305,0.0001008341,0.0003662448,0.0001945351,0.0002691269,0.0003251455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001794014,"about_ca_system_score_gemma":0.0002521163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005181576,"about_ca_topic_score_gemma":0.01347973,"domain_scores_codex":[0.9998941,0.00001315403,0.00000611545,0.00001888236,0.00004430309,0.00002333543],"domain_scores_gemma":[0.9997471,0.00006320827,0.00004468894,0.00002966745,0.00008940239,0.00002590512],"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":[0.001005309,0.0007741989,0.04190608,0.0005385895,0.0003840059,0.0006282576,0.0002035292,0.05608115,0.3578153,0.001272034,0.01089642,0.5284951],"study_design_scores_gemma":[0.00005835002,0.0001513313,0.355828,0.00004108527,0.0002396437,0.0005346172,0.0002354163,0.6020255,0.03180106,0.0011572,0.007859871,0.00006790918],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8377681,0.00262249,0.1423151,0.0005300603,0.0002561759,0.0001326475,0.007072896,0.001329771,0.007972777],"genre_scores_gemma":[0.9166179,0.0007718614,0.07597054,0.00006832081,0.0001147975,0.00007387361,0.004099061,0.0001399111,0.002143627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005181576,"threshold_uncertainty_score":0.01030284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179444924241732,"score_gpt":0.2251383832615932,"score_spread":0.20719389083742,"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."}}