{"id":"W1545539235","doi":"","title":"PHENOTYPIC ANALYSIS OF LONG BONES IN PANNEXIN 3 KNOCKOUT MICE","year":2015,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Pannexin; Phenotype; Biology; Genetics; Evolutionary biology; 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":[0.0002844892,0.0004883138,0.0002816662,0.001242704,0.0003038635,0.00037205,0.0002989581,0.0005405075,0.003870836],"category_scores_gemma":[0.0002341771,0.0003409157,0.0004004175,0.0003530669,0.0004018632,0.0001996229,0.0003699085,0.0006990887,0.0005602351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002097667,"about_ca_system_score_gemma":0.0001371785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004774293,"about_ca_topic_score_gemma":0.001258808,"domain_scores_codex":[0.9997677,0.00002173325,0.00002657871,0.00008055491,0.00007823548,0.00002524411],"domain_scores_gemma":[0.9996542,0.00006118933,0.0001426173,0.00002394139,0.00002993507,0.00008815699],"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":[0.0001859477,0.00004415416,0.001299143,0.00003735754,0.00001540505,0.0005009916,0.0000354904,0.0002108278,0.9946634,0.0001824261,0.0001207867,0.002704119],"study_design_scores_gemma":[0.0001881804,0.001056434,0.2193961,0.0001043502,0.0001672175,0.01431528,0.0002766644,0.006355936,0.7492421,0.0007170344,0.008109075,0.0000717073],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785866,0.0005348567,0.01290484,0.0001799942,0.00003113293,0.00008481641,0.004756665,0.00042057,0.002500519],"genre_scores_gemma":[0.9501246,0.001200554,0.02818789,0.0002323683,0.00001762323,0.0003810939,0.005833641,0.0005682795,0.01345386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003870836,"threshold_uncertainty_score":0.01294929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1448371131310088,"score_gpt":0.3590242793161051,"score_spread":0.2141871661850963,"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."}}