{"id":"W4393432837","doi":"10.5281/zenodo.1239044","title":"Huntingtin Construct Design For Bioid 2018/04/02","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Construct (python library); Huntingtin; Biology; Computational biology; Computer science; Genetics; Programming language; 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":[0.001219916,0.002204049,0.001803563,0.002187585,0.0007103328,0.002174655,0.00380785,0.002191995,0.08324366],"category_scores_gemma":[0.003849938,0.0008556084,0.001377046,0.003214688,0.0004819415,0.001216462,0.001799419,0.002091477,0.07702561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001170789,"about_ca_system_score_gemma":0.002439333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006388772,"about_ca_topic_score_gemma":0.01767077,"domain_scores_codex":[0.999276,0.0001167522,0.00008056321,0.0002680707,0.0001631289,0.00009563642],"domain_scores_gemma":[0.9986689,0.0004593731,0.0001542086,0.0003225679,0.0001815954,0.000213443],"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":[0.0002197576,0.00003754909,0.0006963732,0.001522924,0.00006761872,0.00004788168,0.00003379658,0.0002628081,0.0005855,0.0008701829,0.9933748,0.002280788],"study_design_scores_gemma":[0.0007602562,0.00004082401,0.002362822,0.0003022383,0.0001085166,0.0001176715,0.00004150298,0.0005018359,0.001450176,0.002166568,0.9921131,0.00003457856],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002068593,0.0001358846,0.0002422728,0.00006100716,0.00002448566,0.00002282377,0.9970776,0.001436039,0.000793116],"genre_scores_gemma":[0.0004643469,0.00006581571,0.0004869346,0.0000660927,0.00000354219,0.0001141756,0.9980103,0.0002890067,0.0004997309],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08324366,"threshold_uncertainty_score":0.2784778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0269573713041108,"score_gpt":0.2684836695634247,"score_spread":0.2415262982593139,"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."}}