{"id":"W6903221960","doi":"10.11575/prism/42270","title":"Peeking Through the Windows: Hyperparameters, Administrative Data, and Selective Windowing","year":2023,"lang":"en","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Categorization; Process (computing); Hyperparameter; Time series; Implementation; Duration (music); Preference","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009712355,0.001252424,0.001046457,0.0012295,0.0006285791,0.001916764,0.001605133,0.001413791,0.001154567],"category_scores_gemma":[0.0381714,0.0005998848,0.0009039824,0.001325416,0.001172483,0.004127977,0.001831136,0.00360652,0.0002796257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008815654,"about_ca_system_score_gemma":0.001437882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005305895,"about_ca_topic_score_gemma":0.003859581,"domain_scores_codex":[0.9976775,0.001181561,0.0001906307,0.0005048389,0.0002392181,0.0002062513],"domain_scores_gemma":[0.9818631,0.01390125,0.001226128,0.001955027,0.0006690307,0.0003855937],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001827072,0.000852593,0.06547889,0.0002642554,0.0005456345,0.0003913924,0.0008255261,0.5411325,0.006637849,0.01964209,0.004778059,0.3576241],"study_design_scores_gemma":[0.00007660603,0.0002212521,0.006843784,0.00007723521,0.0001130169,0.0001245844,0.0002282682,0.970626,0.004439317,0.01569941,0.001500014,0.00005050083],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4753137,0.003091638,0.5145466,0.001555461,0.0002379932,0.0002899531,0.000484697,0.001873713,0.002606188],"genre_scores_gemma":[0.900512,0.00046306,0.09705948,0.0001805727,0.00008590548,0.000219642,0.0005246769,0.0001615304,0.0007932569],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9902877,"threshold_uncertainty_score":0.05136442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1571819126601018,"score_gpt":0.3941973058407385,"score_spread":0.2370153931806367,"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."}}