{"id":"W4383162540","doi":"10.59350/g4j1v-y1508","title":"Data Retriever 2.1: Python Interface, Autocomplete &amp;amp; More","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Python (programming language); Labrador Retriever; Computer science; Operating system; Programming language; Database; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.0004374578,0.0003724673,0.0003896772,0.0001213052,0.000341744,0.001351872,0.01006403,0.0001888811,0.0001331101],"category_scores_gemma":[0.00007673845,0.0003610819,0.0001375056,0.0001780986,0.0001146413,0.0005229787,0.01974219,0.0007169654,0.001657048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006475444,"about_ca_system_score_gemma":0.0003855851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002657637,"about_ca_topic_score_gemma":0.0001293932,"domain_scores_codex":[0.9970989,0.0000546874,0.0004460118,0.001530944,0.0005544432,0.0003150312],"domain_scores_gemma":[0.9889367,0.0001738021,0.0004648851,0.009976555,0.000277882,0.0001701421],"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":[0.000008512293,0.0002140612,0.00008002774,0.0001180947,0.0001769013,0.000003785866,0.0005325196,0.006087781,0.0002667547,0.2524654,0.679228,0.06081813],"study_design_scores_gemma":[0.0001057317,0.000003398569,0.001294926,0.00004903616,0.00001306449,0.000005217134,0.000001191312,0.1575315,0.00002238027,0.158727,0.6818464,0.0004001436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000475052,0.000116443,0.9699619,0.008106412,0.0008541061,0.0006548373,0.000518731,0.000450839,0.01886171],"genre_scores_gemma":[0.1233572,0.00007364563,0.8444301,0.001164623,0.0009406878,0.0001837689,0.004304783,0.00008418661,0.02546098],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1514437,"threshold_uncertainty_score":0.9998841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1574689841716247,"score_gpt":0.3878600094580353,"score_spread":0.2303910252864106,"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."}}