{"id":"W4233443931","doi":"10.1109/lgrs.2013.2287063","title":"IEEE Geoscience and Remote Sensing Letters publication information","year":2013,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Computer science; Remote sensing; Data science; Earth science; Information retrieval; World Wide Web; Geology","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000801999,0.0003518685,0.0003048449,0.0004207726,0.001017528,0.001307307,0.0002221433,0.0001323932,0.0000163941],"category_scores_gemma":[0.0001717615,0.0002841601,0.00006691645,0.0007428407,0.0008151794,0.002382433,0.00002593203,0.0003448747,0.0001897508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000210777,"about_ca_system_score_gemma":0.00005853548,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0297934,"about_ca_topic_score_gemma":0.0006346868,"domain_scores_codex":[0.9971598,0.0001438152,0.000499201,0.0006729723,0.0006477067,0.0008764581],"domain_scores_gemma":[0.9985497,0.0001809834,0.000283389,0.0004373603,0.0001585812,0.000389967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009055939,0.000001531823,0.0003276684,0.00002411271,0.000006085536,0.00001357147,0.0007560942,0.0001595832,0.01632505,6.714221e-7,0.002284282,0.9800923],"study_design_scores_gemma":[0.0003889172,0.0000652649,0.07058692,0.0001606439,0.00002568847,0.0006782138,0.0002722165,0.9190778,0.000900549,0.0001687214,0.007043425,0.0006317097],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9099396,0.00005611942,0.07344039,0.01385906,0.001440233,0.0003152049,0.000007875671,0.0001446871,0.0007967879],"genre_scores_gemma":[0.8475423,0.0003134006,0.1188686,0.03242093,0.0004590057,1.320377e-8,0.00005385014,0.00001545432,0.0003263359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9794606,"threshold_uncertainty_score":0.9999611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01036476933107493,"score_gpt":0.195937167580682,"score_spread":0.1855723982496071,"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."}}