{"id":"W2735718245","doi":"10.1186/s13041-017-0312-0","title":"Optimizing reproducibility of operant testing through reinforcer standardization: identification of key nutritional constituents determining reward strength in touchscreens","year":2017,"lang":"en","type":"article","venue":"Molecular Brain","topic":"Neurobiology and Insect Physiology Research","field":"Neuroscience","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Medical Research Council; Yonsei University; National Centre for the Replacement, Refinement and Reduction of Animals in Research; Motor Neurone Disease Association","keywords":"Reinforcement; Caloric theory; Operant conditioning; Calorie; Conditioning; Caloric intake; Sugar; Psychology; Medicine; Food science; Statistics; Endocrinology; Mathematics; Obesity; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001120587,0.0001053104,0.000243549,0.00007980466,0.0002383973,0.00002848589,0.0004249443,0.0000809823,0.00001733553],"category_scores_gemma":[0.01767183,0.0001074526,0.00005706177,0.000163803,0.0007155783,0.0003437728,0.0001988911,0.0001951919,0.000001897918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002358538,"about_ca_system_score_gemma":0.0001184401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004070667,"about_ca_topic_score_gemma":0.000004532861,"domain_scores_codex":[0.9979714,0.0003556917,0.0005317965,0.0007193888,0.0002135634,0.000208221],"domain_scores_gemma":[0.9978617,0.0003527749,0.0003674432,0.001225751,0.0001644601,0.00002784826],"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":[0.00007093869,0.00006336342,0.005906854,0.00004685724,0.000004984885,0.00002798019,0.0001943719,0.001751465,0.9900993,0.001410306,0.00001194566,0.0004116108],"study_design_scores_gemma":[0.0006003576,0.00009353334,0.01597875,0.0001043133,0.000004235923,0.00001350182,0.00002092838,0.001337882,0.9805987,0.001141519,0.00001739263,0.00008881661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994352,0.00001981601,0.003827881,0.0003409309,0.00007091213,0.0002953108,0.00006070333,0.00001420317,0.001018265],"genre_scores_gemma":[0.9976181,0.000008953491,0.002125368,0.0001696977,0.00001565604,0.00001489517,0.00001271765,0.000009069792,0.00002555608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01655124,"threshold_uncertainty_score":0.9906027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07177742383184892,"score_gpt":0.3504902259014676,"score_spread":0.2787128020696187,"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."}}