{"id":"W4393429840","doi":"10.5281/zenodo.833499","title":"Ros-Specific Huntingtin Interactions: Crosslinking Optimization","year":2017,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Genetic Neurodegenerative Diseases","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Huntingtin; Chemistry; Computational biology; Computer science; Biology; Biochemistry; Gene","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":[0.001345056,0.003677881,0.00182845,0.002230363,0.0009862159,0.002003931,0.002146186,0.002501626,0.01275583],"category_scores_gemma":[0.002989508,0.0007346254,0.00307743,0.002511014,0.0003935035,0.001109195,0.001334043,0.001736471,0.01277501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148738,"about_ca_system_score_gemma":0.001479123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007389004,"about_ca_topic_score_gemma":0.02501022,"domain_scores_codex":[0.9989164,0.0001692466,0.00006989738,0.0005169901,0.0001986341,0.0001288264],"domain_scores_gemma":[0.9994264,0.00021253,0.0000552936,0.000165716,0.00009847707,0.00004160189],"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":[0.001898312,0.0005896514,0.01260516,0.004683533,0.001838497,0.0004568879,0.00009919087,0.01635875,0.0118439,0.002068789,0.9017629,0.04579433],"study_design_scores_gemma":[0.00293583,0.0006516582,0.04098323,0.0009061004,0.00238621,0.001881281,0.0002579686,0.09562894,0.03730747,0.01671824,0.8000989,0.0002441173],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03598382,0.007719835,0.009383465,0.0006037938,0.0002786741,0.0002526194,0.9207733,0.01687188,0.00813254],"genre_scores_gemma":[0.02060072,0.0007263141,0.01296157,0.0003039818,0.0000324115,0.0003362423,0.9618196,0.0005840006,0.002635091],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01275583,"threshold_uncertainty_score":0.04267251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08280087460914769,"score_gpt":0.3130451805307,"score_spread":0.2302443059215523,"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."}}