{"id":"W6910741221","doi":"10.5061/dryad.d28080r","title":"Data from: A note on measuring natural selection on principal component scores","year":2018,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multicollinearity; Selection (genetic algorithm); Principal component analysis; Regression; Principal component regression; Regression analysis; Feature selection; Interpretation (philosophy)","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":[0.00817793,0.001731932,0.001219846,0.002732092,0.001144869,0.003673247,0.003739349,0.001952406,0.0167069],"category_scores_gemma":[0.0267574,0.0007904511,0.001832803,0.005370143,0.001128009,0.002437799,0.003279976,0.00351908,0.02457239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00155265,"about_ca_system_score_gemma":0.002434344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01213051,"about_ca_topic_score_gemma":0.02654945,"domain_scores_codex":[0.995347,0.001727209,0.0005214901,0.0008433276,0.001308462,0.0002525952],"domain_scores_gemma":[0.9919796,0.002904113,0.0005071099,0.003164147,0.001178133,0.0002669552],"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":[0.0001950213,0.0000640076,0.006635093,0.001226811,0.0001712016,0.0000899486,0.0001586934,0.002145104,0.0007881337,0.004949727,0.9628494,0.02072675],"study_design_scores_gemma":[0.0002063539,0.00001778933,0.008470241,0.0001723558,0.00002803683,0.0001086267,0.00008176899,0.001619466,0.001034492,0.007817561,0.9803863,0.00005691404],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002782255,0.0008906438,0.007379832,0.001940328,0.0003851873,0.0001571359,0.9768018,0.004677471,0.004985298],"genre_scores_gemma":[0.005936887,0.0003951352,0.02537301,0.0005861075,0.00006652032,0.0007049756,0.9636303,0.001068761,0.002238208],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0167069,"threshold_uncertainty_score":0.05589008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04856258292910402,"score_gpt":0.3063255079014235,"score_spread":0.2577629249723195,"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."}}