{"id":"W2315460111","doi":"10.1080/15481603.2015.1137112","title":"Land change attribution based on Landsat time series and integration of ancillary disturbance data in the Athabasca oil sands region of Canada","year":2016,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Canadian Forest Service; Canadian Space Agency","keywords":"Disturbance (geology); Environmental science; Land cover; Ecosystem; Physical geography; Flooding (psychology); Environmental change; Change detection; Climate change; Land use, land-use change and forestry; Hydrology (agriculture); Logging; Land use; Geography; Ecology; Remote sensing; Forestry; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006074191,0.0001180489,0.0001491697,0.00002825771,0.000125283,0.00002195075,0.0002432605,0.00004204238,0.0000032076],"category_scores_gemma":[0.0002091502,0.00006029961,0.00001379846,0.0003921342,0.0003974005,0.0003245795,0.0001075471,0.00007703279,0.000001209489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001592879,"about_ca_system_score_gemma":0.00005001504,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.083753,"about_ca_topic_score_gemma":0.2796112,"domain_scores_codex":[0.9986508,0.0001154784,0.0001919098,0.0003313305,0.0005016665,0.0002087695],"domain_scores_gemma":[0.9991583,0.0001245535,0.0001619496,0.0004956622,0.00002222477,0.00003726932],"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":[0.0002195443,0.000046605,0.03808359,0.00004900184,0.000003502528,0.00007747631,0.001794017,0.0002900483,0.1959703,0.00001485233,0.002707898,0.7607431],"study_design_scores_gemma":[0.0006388922,0.0003214968,0.8354877,0.00120508,0.00001691279,0.0001966883,0.0002164377,0.1381132,0.01940853,0.0001081113,0.003928404,0.0003586348],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926668,0.00003848712,0.002638875,0.003406022,0.00008908618,0.0001174038,0.00002685427,0.000009319742,0.001007147],"genre_scores_gemma":[0.9977441,0.00003293777,0.001874889,0.0001716198,0.00003353882,2.820496e-8,0.00001741491,0.00000430659,0.0001212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7974041,"threshold_uncertainty_score":0.9223484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02144578206542079,"score_gpt":0.2121658201529646,"score_spread":0.1907200380875438,"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."}}