{"id":"W4353080502","doi":"10.21203/rs.3.rs-2668590/v1","title":"Sequence Spinning Axially Encoded Metafibers","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Sensor and Energy Harvesting Materials","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute of Nutrition, Metabolism and Diabetes; Fundamental Research Funds for the Central Universities; Zhejiang University; National Natural Science Foundation of China","keywords":"Spinning; Fiber; Encoding (memory); Sequence (biology); Axial symmetry; Scope (computer science); Computer science; Code (set theory); ENCODE; Materials science; Engineering; Biology; Artificial intelligence; Structural engineering; Composite material","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001281543,0.00032255,0.0001619249,0.0002468061,0.000235909,0.0003902179,0.0003523783,0.0004037766,0.002604151],"category_scores_gemma":[0.0002953513,0.0001274063,0.00009434073,0.0002343869,0.0002809753,0.0005284713,0.0003803156,0.0003740573,0.0005613923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000337118,"about_ca_system_score_gemma":0.0001871209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002015065,"about_ca_topic_score_gemma":0.0006153355,"domain_scores_codex":[0.9998845,0.00001552851,0.000005399079,0.00002960617,0.0000453272,0.00001975436],"domain_scores_gemma":[0.9996407,0.00006455924,0.0001053164,0.00007387065,0.00006243373,0.00005312783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002369717,0.00003429018,0.0001777397,0.00004911874,0.000004160287,0.00007205577,0.0000359322,0.001759078,0.9646212,0.01794356,0.0003779907,0.01468782],"study_design_scores_gemma":[0.00002326657,0.0002721547,0.000645596,0.00001173909,0.000006493464,0.0001463972,0.00002603611,0.02965937,0.9569153,0.004142212,0.008124646,0.00002686612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9149038,0.0006572418,0.06161159,0.00038488,0.0003155639,0.00004653339,0.0003492651,0.0006627878,0.02106838],"genre_scores_gemma":[0.926955,0.0003057229,0.06181816,0.00009343747,0.00007050999,0.00003235583,0.000179539,0.00008067959,0.01046464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002604151,"threshold_uncertainty_score":0.008711755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1890197384043497,"score_gpt":0.4046694477928315,"score_spread":0.2156497093884817,"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."}}