{"id":"W7083581546","doi":"10.5281/zenodo.17211957","title":"HuR knockdown dataset used in DENetwork analysis","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Agriculture, Water, and Health","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007395654,0.0002675941,0.0003706965,0.000362833,0.001279812,0.000453586,0.001938848,0.000213236,0.05041312],"category_scores_gemma":[0.0001277517,0.0002426437,0.00008944987,0.002289322,0.0001457624,0.0001840863,0.002818542,0.0006068225,0.008650931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004872188,"about_ca_system_score_gemma":0.000004887674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001213314,"about_ca_topic_score_gemma":0.0002095498,"domain_scores_codex":[0.9971961,0.0004979508,0.00041689,0.0007794995,0.0005483507,0.0005612089],"domain_scores_gemma":[0.9985636,0.0000297391,0.0001808891,0.0009572069,0.00004336258,0.0002252273],"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":[0.00001982166,0.0001573793,0.00006087774,0.00006671511,0.00006095285,0.0000231115,0.00009366839,0.0005996937,0.000009495139,0.00001227379,0.9975519,0.00134417],"study_design_scores_gemma":[0.0002787675,0.00006690424,0.002384881,0.00002550473,0.0001415259,0.000006684361,0.00005485876,0.00006272486,0.000006339771,0.00002349474,0.9966897,0.0002586202],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003540858,0.00005500748,0.00008731422,0.0001808058,0.00008229224,0.000442692,0.988479,0.0001282858,0.01019055],"genre_scores_gemma":[0.0009412608,0.0005359878,0.00005448955,0.0002967022,0.0001076302,9.145307e-8,0.9969676,0.0001797142,0.0009164959],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04176219,"threshold_uncertainty_score":0.9921209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02212125611538696,"score_gpt":0.2584958177910203,"score_spread":0.2363745616756334,"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."}}