{"id":"W4398263783","doi":"10.7910/dvn/pkjufn/j4wqs5","title":"FCC2001.022.ran","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Transport Systems and Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Ran; Computer science; Computer network","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":[0.00005886664,0.0003737858,0.0004942646,0.0001207597,0.0000434766,0.00003541779,0.0007128434,0.0005302899,0.01136528],"category_scores_gemma":[0.00002172726,0.0003955178,0.0001157053,0.0001877789,0.00005154447,0.0001149233,0.00008895387,0.0006664729,0.27862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005281257,"about_ca_system_score_gemma":0.00002916957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001493377,"about_ca_topic_score_gemma":0.0002500121,"domain_scores_codex":[0.9986962,0.00001266759,0.0003578963,0.0003710854,0.0002015936,0.000360569],"domain_scores_gemma":[0.9986099,0.00001905941,0.00005548209,0.001151627,0.00001578224,0.0001481238],"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":[0.000005054024,0.00001137524,0.000002362216,0.0004350948,0.0001127397,0.0003989112,0.000008327015,0.00005438744,0.00003764002,0.00003497428,0.9986507,0.0002484809],"study_design_scores_gemma":[0.0002698781,0.00002249014,0.00001199403,0.00006689147,0.00009617727,0.00002824569,0.00001698155,0.00007081615,0.00003347509,0.000008589618,0.99898,0.0003944683],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000009334899,0.000006109575,0.0001344951,0.000004315714,0.001378127,0.0002138778,0.9970269,0.0008422296,0.0003845655],"genre_scores_gemma":[0.000114468,0.0006732683,0.0001537259,0.00009432899,0.0003287852,0.00003210485,0.9984804,0.00005798258,0.00006498445],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2672548,"threshold_uncertainty_score":0.9998497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027875117288524,"score_gpt":0.1924027314737387,"score_spread":0.1821239803008534,"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."}}