{"id":"W6901786500","doi":"10.60692/ggq9y-pyj57","title":"Task-Informed Meta-Learning data","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Data set; Codebase; Data collection; Identification (biology); Work (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":[0.004087351,0.001910426,0.001075657,0.002690866,0.0008780746,0.00285111,0.003420501,0.002427787,0.07345637],"category_scores_gemma":[0.03113055,0.00101442,0.001681815,0.002742209,0.0005885333,0.002562121,0.002721473,0.004087777,0.07751288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001573147,"about_ca_system_score_gemma":0.004649548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008642866,"about_ca_topic_score_gemma":0.02219593,"domain_scores_codex":[0.9980552,0.0004040433,0.0001767583,0.000633957,0.0005812264,0.0001488275],"domain_scores_gemma":[0.9909436,0.003163306,0.0004032103,0.003070094,0.002007719,0.0004121378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006314597,0.0003240654,0.004565236,0.001387957,0.0002120224,0.0001597311,0.0001961305,0.008308611,0.002459773,0.004519985,0.9225555,0.05467949],"study_design_scores_gemma":[0.0007046227,0.0001663922,0.005597889,0.000941536,0.0002065725,0.000307558,0.0001674416,0.03579355,0.01257226,0.04388635,0.899451,0.0002047684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004365308,0.0008428658,0.03730848,0.001141647,0.0006365034,0.0004128111,0.9028401,0.04244114,0.01001116],"genre_scores_gemma":[0.02046885,0.0003910376,0.05919848,0.0007473034,0.00008708852,0.001525635,0.9026623,0.007518495,0.007400742],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07345637,"threshold_uncertainty_score":0.2457361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1070936500499923,"score_gpt":0.260133056864539,"score_spread":0.1530394068145468,"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."}}