{"id":"W4394408138","doi":"10.6084/m9.figshare.654049.v2","title":"Facilitating analysis of genomic variation in Olympia oysters","year":2013,"lang":"en","type":"dataset","venue":"Figshare","topic":"Marine Bivalve and Aquaculture Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variation (astronomy); Oyster; Biology; Computational biology; Evolutionary biology; Fishery","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001332184,0.0005386355,0.0008759479,0.001829945,0.0008586175,0.001526915,0.001034551,0.0005843577,0.01710652],"category_scores_gemma":[0.003557174,0.0005966785,0.001006243,0.003189307,0.0002786583,0.0005162696,0.002476523,0.001230775,0.008136102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006852503,"about_ca_system_score_gemma":0.001272025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01544379,"about_ca_topic_score_gemma":0.03387456,"domain_scores_codex":[0.9988241,0.0001102649,0.00009374924,0.0005985378,0.000260712,0.0001127029],"domain_scores_gemma":[0.9983876,0.0006703204,0.00016437,0.000356873,0.0002796377,0.0001412045],"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":[0.002522412,0.0003422192,0.1748064,0.004591394,0.0009554701,0.001626584,0.004456317,0.01055386,0.1296684,0.00643051,0.5185964,0.1454501],"study_design_scores_gemma":[0.0004248542,0.0001433814,0.4293192,0.000601036,0.0003071697,0.0004380084,0.0009604962,0.005778147,0.01487976,0.004714855,0.5422806,0.0001525029],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06125955,0.0002670351,0.007790967,0.0001954731,0.00009940458,0.0001110014,0.917397,0.00426549,0.008614046],"genre_scores_gemma":[0.0351291,0.0001574631,0.01847452,0.0001730639,0.00002738099,0.0002644072,0.9411497,0.001773076,0.002851355],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01710652,"threshold_uncertainty_score":0.05722708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02276166968193756,"score_gpt":0.2497012301071483,"score_spread":0.2269395604252107,"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."}}