{"id":"W4393520862","doi":"10.5281/zenodo.4762586","title":"SC-Earth: A Station-based Serially Complete Earth Dataset from 1950 to 2019","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Geological Modeling and Analysis","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Earth (classical element); Earth observation; Geology; Remote sensing; Geodesy; Astrobiology; Computer science; Engineering; Satellite; Physics; Astronomy; Aerospace engineering","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008713776,0.0003530855,0.0005246474,0.0001363932,0.0001895056,0.0003308387,0.0007273615,0.0002584483,0.8283301],"category_scores_gemma":[0.0009426263,0.0003031242,0.0001541626,0.0003437825,0.000009273172,0.00009202078,0.00007524685,0.0003715142,0.08555909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002705559,"about_ca_system_score_gemma":0.0002723645,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0117695,"about_ca_topic_score_gemma":0.05030382,"domain_scores_codex":[0.9976764,0.0001930945,0.0003781419,0.000797951,0.0005272964,0.000427167],"domain_scores_gemma":[0.9980368,0.0004481586,0.0001791993,0.0008427891,0.0001390752,0.0003539573],"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":[0.00001732418,0.00001542989,0.00003148285,0.00004406493,0.00004292961,0.00008848491,0.000002323219,0.0290175,5.881547e-7,1.20456e-8,0.970088,0.0006518498],"study_design_scores_gemma":[0.0001693199,0.00007645065,0.00228632,0.000540346,0.00005920008,0.000001023523,0.000005363804,0.008649947,0.000002249417,0.000006082232,0.9877946,0.0004090324],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002705266,0.0004022992,0.000005865422,0.0003168735,0.0002306999,0.0001840476,0.9987661,0.0000375004,0.00002960079],"genre_scores_gemma":[0.00006591329,0.00001312859,0.0006568683,0.003125426,0.0005362533,0.00001023858,0.9954848,0.000005638746,0.0001017863],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.742771,"threshold_uncertainty_score":0.9999421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06267323065593154,"score_gpt":0.2500278130540256,"score_spread":0.187354582398094,"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."}}