{"id":"W1983982900","doi":"10.14358/pers.75.8.941","title":"The Land-cover Change Mapper (LCM) and its Application to Timber Harvest Monitoring in Western Canada","year":2009,"lang":"en","type":"article","venue":"Photogrammetric Engineering & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Land cover; Geography; Forestry; Thematic Mapper; Cover (algebra); Forest cover; Remote sensing; Cartography; Land use; Change analysis; Physical geography; Ecology; Satellite imagery; Engineering; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002488976,0.0002345093,0.0001809807,0.0001106726,0.0001272984,0.000109183,0.000146726,0.00008174184,0.00000147679],"category_scores_gemma":[0.0001334354,0.0001849998,0.00002931416,0.001408773,0.00001254106,0.0001232593,0.00008445001,0.0002670875,0.00003113554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004758009,"about_ca_system_score_gemma":0.000009736646,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2192672,"about_ca_topic_score_gemma":0.0951653,"domain_scores_codex":[0.9984519,0.00002563596,0.0002329198,0.0003818038,0.0003837844,0.0005239426],"domain_scores_gemma":[0.9993742,0.0001151835,0.00005638105,0.0002565274,0.00001727869,0.0001804449],"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":[0.00001555323,0.00001689631,0.01084591,0.00002065246,0.00001278484,0.00006624938,0.0005566979,0.08124181,0.05644394,0.000001580813,0.0002019234,0.850576],"study_design_scores_gemma":[0.0002702996,0.00003639816,0.577226,0.0001274519,0.00001445969,0.00009801023,0.00002492994,0.3749071,0.008374467,0.000008472888,0.0384025,0.0005099219],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918405,0.000303171,0.005703704,0.0006684625,0.0003336028,0.0006282301,0.00000127355,0.00008963393,0.0004314441],"genre_scores_gemma":[0.9945388,0.0000660173,0.004785559,0.0002288977,0.0001841834,3.81792e-7,0.000002400406,0.00002483076,0.0001689105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8500661,"threshold_uncertainty_score":0.9213456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00876793474753676,"score_gpt":0.2036723020798066,"score_spread":0.1949043673322699,"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."}}