{"id":"W4393820022","doi":"10.5281/zenodo.7277853","title":"Model 4 dataset for the manuscript \"Improving trajectory calculations by FLEXPART 10.4+ using deep learning inspired single image superresolution\"","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Superresolution; Trajectory; Artificial intelligence; Image (mathematics); Deep learning; Computer science; Pattern recognition (psychology); Computer vision; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006882534,0.002806521,0.001055533,0.001170998,0.0008656444,0.001249122,0.003387348,0.002353497,0.04946141],"category_scores_gemma":[0.002672184,0.0004634937,0.001475252,0.001758217,0.0004584018,0.001071997,0.001281076,0.002104667,0.07020646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001611259,"about_ca_system_score_gemma":0.002122174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03283844,"about_ca_topic_score_gemma":0.08408529,"domain_scores_codex":[0.999405,0.00009083583,0.00003708594,0.0001656724,0.0001914298,0.0001099141],"domain_scores_gemma":[0.9991921,0.000122082,0.00005136546,0.0002509794,0.0002972274,0.00008636876],"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":[0.00007889074,0.00005353564,0.0003252331,0.0001734629,0.00002964877,0.00002806682,0.000006216367,0.00200526,0.0002706688,0.0003595576,0.9923034,0.004366043],"study_design_scores_gemma":[0.0007386568,0.0001243343,0.004292872,0.0001880817,0.00007022559,0.0001624151,0.00009487865,0.021996,0.004007076,0.006194807,0.9620347,0.00009609287],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001669514,0.0001695635,0.001571354,0.0003729016,0.0002668303,0.00008513888,0.9880487,0.004308252,0.003507661],"genre_scores_gemma":[0.001849501,0.00004719543,0.002166845,0.00009020046,0.0000156421,0.0001069262,0.9933879,0.0002538709,0.002081859],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04946141,"threshold_uncertainty_score":0.1654649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06404840493235264,"score_gpt":0.2478179311653753,"score_spread":0.1837695262330226,"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."}}