{"id":"W2763365034","doi":"","title":"Multi-Scale Change Detection Research of Remotely Sensed Big Data in CyberGIS","year":2015,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Scale (ratio); Change detection; Remote sensing; Big data; Computer science; Geography; Data science; Environmental science; Cartography; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003052485,0.0001191495,0.0001966161,0.0002354996,0.00008977508,0.00004859089,0.0003122853,0.0001335345,0.000001805097],"category_scores_gemma":[0.001124356,0.0001011201,0.00002447238,0.0004808682,0.00008299752,0.000189739,0.00004332183,0.000367138,0.0001105167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001020467,"about_ca_system_score_gemma":0.00007074861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4629748,"about_ca_topic_score_gemma":0.6418206,"domain_scores_codex":[0.9980196,0.0002912102,0.000353191,0.0003876671,0.0005175625,0.0004307879],"domain_scores_gemma":[0.9985989,0.0003817398,0.0001247221,0.0005585657,0.0001406428,0.0001954012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001445795,0.00007091156,0.5898457,0.00004849984,0.000009851184,0.0001194516,0.002392224,0.002664135,0.001769117,5.704464e-8,0.0001863556,0.4027492],"study_design_scores_gemma":[0.0005224867,0.0001001904,0.9502175,0.0001482795,0.000005475262,0.00002238229,0.0006298862,0.04486663,0.0009421304,0.00002298626,0.002386275,0.0001357512],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990828,0.0003386713,0.000006345607,0.0001386915,0.0005196686,0.0001591994,0.00002823126,0.00003962688,0.007941581],"genre_scores_gemma":[0.9964194,0.00007572678,0.002914436,0.00003575305,0.0003415763,1.100127e-7,0.00009851413,0.000006821389,0.0001077176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4026134,"threshold_uncertainty_score":0.5406013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2952084339393663,"score_gpt":0.3367241729598173,"score_spread":0.04151573902045097,"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."}}