{"id":"W4414422418","doi":"","title":"POCO: Scalable Neural Forecasting through Population Conditioning.","year":2025,"lang":"en","type":"preprint","venue":"PubMed","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Interpretability; Context (archaeology); Artificial neural network; Scalability; Population; Key (lock); Session (web analytics)","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":[0.0008602053,0.0008806759,0.000695591,0.0003625513,0.0004030908,0.0008164303,0.002455611,0.001120877,0.008983891],"category_scores_gemma":[0.004844269,0.0005429666,0.0005980799,0.000458005,0.0005465314,0.001662622,0.001568454,0.001946017,0.002109935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007388978,"about_ca_system_score_gemma":0.001635894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008386288,"about_ca_topic_score_gemma":0.01403363,"domain_scores_codex":[0.9998205,0.00002519359,0.000009447327,0.00006443891,0.00005166654,0.00002889261],"domain_scores_gemma":[0.9991252,0.0003805663,0.00006432001,0.0002204275,0.0001249224,0.00008456117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003507643,0.0001159874,0.00345076,0.0003477449,0.0002029369,0.0001550131,0.000119827,0.6702241,0.008082136,0.02204322,0.04353779,0.2513696],"study_design_scores_gemma":[0.00001197201,0.00001192856,0.0001313144,0.000007689461,0.00000648207,0.0000140217,0.000005142926,0.9905612,0.00124133,0.006662545,0.00134003,0.000006347711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0237234,0.0007363719,0.942473,0.0008197312,0.0004953973,0.0001456495,0.002373396,0.0217306,0.007502384],"genre_scores_gemma":[0.6014578,0.0007544454,0.3773865,0.0009061694,0.0003273602,0.0006784822,0.00583341,0.002399304,0.01025651],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008983891,"threshold_uncertainty_score":0.03005409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05761907065305844,"score_gpt":0.2619920832624037,"score_spread":0.2043730126093453,"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."}}