{"id":"W4394690884","doi":"10.20944/preprints202404.0485.v1","title":"Optimizing Learning: Predicting Research Competency via Statistical Proficiency","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Chulalongkorn University","keywords":"Mathematics education; Cheating; Psychology; Statistical thinking; Statistical hypothesis testing; Statistical analysis; Statistics education; Computer science; Medical education; Statistics; Mathematics; Social psychology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005547899,0.0006320622,0.0003023196,0.001277861,0.000330199,0.002314995,0.0004986271,0.0007828567,0.002220375],"category_scores_gemma":[0.04448771,0.0001610237,0.0005566733,0.0008572371,0.0008227674,0.001534458,0.001236162,0.001025032,0.0009943433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000444918,"about_ca_system_score_gemma":0.001254895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00112102,"about_ca_topic_score_gemma":0.001212361,"domain_scores_codex":[0.9976662,0.001119139,0.000233524,0.0003957053,0.0004152148,0.0001702091],"domain_scores_gemma":[0.9466496,0.03551134,0.008778599,0.003287932,0.003624207,0.00214848],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002708101,0.001211828,0.9171385,0.0001180637,0.00009976028,0.00008168967,0.0007743042,0.007806609,0.002854349,0.00112272,0.000662742,0.06785861],"study_design_scores_gemma":[0.00004386059,0.001472286,0.902573,0.000133311,0.0000805946,0.0001964048,0.001171148,0.07000344,0.01063196,0.01134161,0.002281919,0.00007047704],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865928,0.00008094071,0.01037937,0.000168346,0.000007339369,0.0000731463,0.0001562918,0.00008733735,0.002454415],"genre_scores_gemma":[0.9919442,0.00005494548,0.007066081,0.00003035274,0.000007911842,0.00005670023,0.0002510642,0.00001058332,0.0005782061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9944521,"threshold_uncertainty_score":0.02934045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6284418545439442,"score_gpt":0.5613395034478346,"score_spread":0.06710235109610962,"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."}}