{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001504958,0.003580347,0.0009126832,0.001605,0.000630802,0.001335585,0.003383471,0.002153285,0.01555911],"category_scores_gemma":[0.003657489,0.0007831429,0.002319332,0.001605707,0.0008413815,0.00123962,0.001813543,0.003318464,0.02095818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001940635,"about_ca_system_score_gemma":0.001366882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02927307,"about_ca_topic_score_gemma":0.06598885,"domain_scores_codex":[0.9991978,0.0001424518,0.00004530129,0.0002717567,0.0002206983,0.0001220332],"domain_scores_gemma":[0.9991246,0.0002126241,0.00003904684,0.0003589906,0.0002139093,0.00005077494],"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":[0.000394608,0.0002853425,0.004068843,0.0007434826,0.0002877956,0.0002641329,0.00007561935,0.05479687,0.002475219,0.002281919,0.8681635,0.06616267],"study_design_scores_gemma":[0.0006997312,0.0002896354,0.01509651,0.0005155919,0.0002000039,0.001046768,0.0002354782,0.4029781,0.01926836,0.01496964,0.5444592,0.0002411277],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0328985,0.002918388,0.03983396,0.001704545,0.001733944,0.0008574457,0.872408,0.03299312,0.01465206],"genre_scores_gemma":[0.02733373,0.0004981693,0.02763656,0.0004233605,0.00006431158,0.0005142993,0.9342747,0.0008083518,0.008446584],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02927307,"threshold_uncertainty_score":0.05820537,"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."}}