{"id":"W4403501353","doi":"10.1038/s41598-024-74450-0","title":"Using “Wordle” to assess the effects of goal gradients and near-misses","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Mental Health Research Topics","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Near miss; Computer science; Reliability engineering; Engineering","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":[0.001019766,0.0009744462,0.0004145804,0.0006219418,0.0003244551,0.0009317948,0.000418418,0.0008165821,0.005907027],"category_scores_gemma":[0.01015261,0.000293254,0.0003331825,0.000287521,0.0006833933,0.0007985972,0.001014818,0.001263184,0.0008928338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002877948,"about_ca_system_score_gemma":0.0001958078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005519873,"about_ca_topic_score_gemma":0.0009997635,"domain_scores_codex":[0.9987231,0.0003831122,0.000193498,0.000225349,0.0003618009,0.0001131582],"domain_scores_gemma":[0.9926191,0.003715774,0.001952074,0.0005714198,0.0004829016,0.0006586956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01760403,0.02999621,0.3292717,0.002471897,0.001019361,0.0009730103,0.01176055,0.006267427,0.4006018,0.008147095,0.008588636,0.1832983],"study_design_scores_gemma":[0.0006330645,0.03881368,0.8631509,0.0001794353,0.0003964065,0.0009354569,0.003404492,0.01374226,0.06217249,0.00723961,0.009085295,0.0002469312],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905272,0.00005242769,0.003768233,0.00006353475,0.00004482026,0.0002579718,0.0003509045,0.0001296093,0.004805425],"genre_scores_gemma":[0.9790609,0.00009772622,0.01279501,0.0002770802,0.00002144458,0.001726709,0.0007327391,0.00008777006,0.005200497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005907027,"threshold_uncertainty_score":0.01976097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1209049636276727,"score_gpt":0.4721723074624256,"score_spread":0.3512673438347529,"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."}}