{"id":"W7134937095","doi":"10.5281/zenodo.18960828","title":"Ep. 3: Safetensors or something else: STT inference formats explained","year":2025,"lang":"en","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prompt (Canada)","funders":"","keywords":"Inference; Feature (linguistics); Identification (biology); Noise (video)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001093453,0.001200285,0.0005495444,0.0007638781,0.001373818,0.004892588,0.001697788,0.001791736,0.7566492],"category_scores_gemma":[0.008472615,0.001053655,0.0008206294,0.0006956483,0.0006370398,0.006214957,0.002949891,0.003724507,0.5978052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001511028,"about_ca_system_score_gemma":0.001154526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008862647,"about_ca_topic_score_gemma":0.01504666,"domain_scores_codex":[0.999248,0.0001452259,0.00006102463,0.0001100051,0.0002914389,0.0001442523],"domain_scores_gemma":[0.9979547,0.0006624584,0.00006381916,0.0003828551,0.0007583452,0.0001779107],"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":[0.00008440515,0.00001482338,0.00005838211,0.00009111902,0.000001676269,0.0000504745,0.0002713039,0.0001052028,0.0003344452,0.00716615,0.9731407,0.01868131],"study_design_scores_gemma":[0.00003111113,0.00001453689,0.0001968956,0.0001043091,0.000003607184,0.00007141552,0.0002739081,0.0005070393,0.001185908,0.005292717,0.9922937,0.00002473967],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001028312,0.0002312659,0.04678737,0.004216296,0.003272666,0.0004158042,0.04102306,0.1004023,0.802623],"genre_scores_gemma":[0.01996309,0.0003436971,0.02772275,0.002406884,0.0008716409,0.0005607807,0.03162613,0.1235708,0.7929343],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7566492,"threshold_uncertainty_score":0.3471103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07298356215174001,"score_gpt":0.3781744684945319,"score_spread":0.3051909063427919,"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."}}