{"id":"W4398620145","doi":"10.7910/dvn/hbikkv/yo4jec","title":"RunC.nc","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cloud computing; Seeding; Turbulence; Meteorology; Environmental science; Cloud seeding; Mechanics; Statistical physics; Atmospheric sciences; Computer science; Physics; Thermodynamics; Operating system","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009732391,0.003490295,0.002008394,0.003267271,0.0008547654,0.003708167,0.004093655,0.001964411,0.152789],"category_scores_gemma":[0.004841831,0.0009613609,0.001660822,0.006941106,0.000557656,0.002671664,0.002517354,0.002417295,0.2348111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001542903,"about_ca_system_score_gemma":0.00209375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04269295,"about_ca_topic_score_gemma":0.04923821,"domain_scores_codex":[0.9990659,0.000141361,0.00008240371,0.0003313577,0.0001935273,0.0001853974],"domain_scores_gemma":[0.9983909,0.0002786098,0.0001205282,0.0005984326,0.000374874,0.0002365848],"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":[0.00003598666,0.000007641861,0.0001640934,0.0001877365,0.00002215622,0.000004607773,0.000007166558,0.000164987,0.00003692321,0.0003489988,0.998117,0.000902746],"study_design_scores_gemma":[0.0002414851,0.00001189206,0.001549565,0.0001546132,0.00002540914,0.00001815943,0.00003491565,0.0006617287,0.000322519,0.001994827,0.9949527,0.00003228044],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004248417,0.00003723822,0.00004507772,0.00004627738,0.00002783099,0.000004179974,0.9977344,0.001238668,0.0008237315],"genre_scores_gemma":[0.0002560643,0.00003863391,0.0001490125,0.00003875484,0.000009264615,0.00002526977,0.9985701,0.0003482137,0.0005646333],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.847211,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01011004571561344,"score_gpt":0.2093313592842814,"score_spread":0.199221313568668,"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."}}