{"id":"W4388290042","doi":"10.1002/etc.5781","title":"Optimizing Sex Ratios of <i>Hyalella azteca</i> to Reduce Variability in Reproduction and Improve Reproductive Toxicity Test Methods","year":2023,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Reproductive biology and impacts on aquatic species","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; University of Guelph","keywords":"Hyalella azteca; Reproduction; Toxicity; Reproductive toxicity; Biology; Toxicology; Aquatic toxicology; Test (biology); Ecology; Zoology; Environmental science; Crustacean; Amphipoda; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003549193,0.0005131713,0.0003245454,0.0003730987,0.000202555,0.0004249164,0.0005179998,0.0003671966,0.001513108],"category_scores_gemma":[0.0005114451,0.0001705506,0.0002515935,0.0002474312,0.0002065292,0.0003597547,0.0004517152,0.0006686533,0.0004312616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004538151,"about_ca_system_score_gemma":0.0002948354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001179415,"about_ca_topic_score_gemma":0.002566784,"domain_scores_codex":[0.9995568,0.00006448111,0.00005367249,0.0001042283,0.0001695614,0.00005121181],"domain_scores_gemma":[0.9996167,0.00008414083,0.0001173834,0.00003369649,0.0000921301,0.00005592921],"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":[0.00005895459,0.00004119267,0.0006139171,0.00004603203,0.000006092775,0.00002027529,0.000009421729,0.0001254576,0.9956368,0.00003466412,0.00004703767,0.003360227],"study_design_scores_gemma":[0.00002042741,0.0009800541,0.01093341,0.00001118493,0.00002842045,0.00009771735,0.0000359466,0.0007969752,0.9836204,0.00004747811,0.003414687,0.00001339233],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9854493,0.001338377,0.008120258,0.0002443946,0.00006451341,0.0002962179,0.001024215,0.000228457,0.003234264],"genre_scores_gemma":[0.9772192,0.001051078,0.01494607,0.0003555978,0.00001719245,0.0002927359,0.001736102,0.0000825405,0.004299401],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001513108,"threshold_uncertainty_score":0.005061805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01073078431863384,"score_gpt":0.2809035025577828,"score_spread":0.270172718239149,"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."}}