{"id":"W4393584136","doi":"10.5281/zenodo.4171627","title":"4x and 10x Super Resolution Generator Models Trained With Planet CubeSat Satellite Imagery","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Spacecraft Design and Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"CubeSat; Planet; Satellite imagery; Satellite; Remote sensing; Mental image; Generator (circuit theory); Computer science; Artificial intelligence; Geology; Psychology; Engineering; Astronomy; Aerospace engineering; Physics; Neuroscience; Power (physics)","routes":{"ca_aff":false,"ca_fund":false,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001906869,0.0003011391,0.0002894872,0.0003101722,0.0005237826,0.0004407662,0.0005934849,0.0002608945,0.001769516],"category_scores_gemma":[0.0000679728,0.0003007589,0.00002456185,0.0002901342,0.0001824037,0.0001992907,0.0003821199,0.0006122501,0.00300597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001150684,"about_ca_system_score_gemma":0.000004987972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000171276,"about_ca_topic_score_gemma":0.000002077036,"domain_scores_codex":[0.9984767,0.0001288971,0.0002341792,0.0004749309,0.0002797303,0.0004055134],"domain_scores_gemma":[0.9991336,0.00001682778,0.00004933986,0.0004864082,0.0001151873,0.0001985807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005904604,0.00001971054,1.148624e-7,0.0001827601,0.00007399362,0.00008444209,0.0001077087,0.0004558373,0.001478257,0.0001294389,0.9919716,0.005437076],"study_design_scores_gemma":[0.0004295577,0.0002051028,0.000007792149,0.00003490025,0.00004238512,0.0002232478,0.0000404947,0.005063808,0.0001505358,0.0000356415,0.9934335,0.000333023],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002979017,0.0008255314,0.002687497,0.0003190586,0.0001163339,0.0006105482,0.9888633,0.002456037,0.003823731],"genre_scores_gemma":[0.0017077,0.002223136,0.000381798,0.0001311839,0.0002102326,1.373489e-7,0.9938464,0.0014164,0.00008299915],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.005104053,"threshold_uncertainty_score":0.9999444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02422400840726654,"score_gpt":0.1902890592695532,"score_spread":0.1660650508622867,"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."}}