{"id":"W4235160750","doi":"10.1002/9780470015902.a0005186.pub2","title":"Genetic Discrimination","year":2014,"lang":"en","type":"other","venue":"Encyclopedia of Life Sciences","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Context (archaeology); Normative; Genetic testing; Actuarial science; Variety (cybernetics); Business; Genetic discrimination; Order (exchange); Public economics; Risk analysis (engineering); Economics; Political science; Medicine; Law; Computer science","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":[0.004861063,0.0005838035,0.0005561247,0.001065881,0.002658634,0.003376451,0.001416212,0.004711761,0.06954513],"category_scores_gemma":[0.01701938,0.0002046817,0.0005944658,0.0008698874,0.004628088,0.002143855,0.003792738,0.003780153,0.01667554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001935775,"about_ca_system_score_gemma":0.00300371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003495723,"about_ca_topic_score_gemma":0.00368276,"domain_scores_codex":[0.9939494,0.002049522,0.0002917231,0.001063869,0.001663227,0.000982263],"domain_scores_gemma":[0.9927054,0.002601632,0.000729685,0.001391664,0.001715189,0.0008563666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009155469,0.00009198589,0.009300525,0.0001567212,0.00002412268,0.001675633,0.002481968,0.0001967526,0.0007237935,0.680774,0.1594175,0.1450655],"study_design_scores_gemma":[0.00003508075,0.00008669687,0.005714364,0.0005187881,0.00002782597,0.004378599,0.001472386,0.0002751255,0.0007200259,0.1728961,0.8138316,0.00004329181],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0220678,0.005588508,0.01264239,0.07394981,0.005941157,0.0002152142,0.001202316,0.0002309922,0.8781618],"genre_scores_gemma":[0.445095,0.007432861,0.00900018,0.09115389,0.003646097,0.0004084265,0.001703205,0.0002103966,0.4413499],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.06954513,"threshold_uncertainty_score":0.2326517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0289804121284884,"score_gpt":0.3022095006823748,"score_spread":0.2732290885538864,"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."}}