{"id":"W6949600847","doi":"10.5281/zenodo.14710229","title":"INSTAR Rollup #1 - AIOTI Days 2024","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data and Digital Economy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Key (lock); Instar; Emerging technologies; Information technology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003127792,0.001492327,0.0007142583,0.001682989,0.002320997,0.006072538,0.001599599,0.003249806,0.1844776],"category_scores_gemma":[0.003959657,0.0005448259,0.0008971705,0.0008975386,0.0005801879,0.002644699,0.003806054,0.003357287,0.1387184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001808366,"about_ca_system_score_gemma":0.004982688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006751206,"about_ca_topic_score_gemma":0.01224042,"domain_scores_codex":[0.9977745,0.0002228644,0.00005022678,0.0002459071,0.001212735,0.0004937681],"domain_scores_gemma":[0.9965746,0.0003060055,0.0001817721,0.0005019579,0.001281847,0.001153796],"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":[0.0008690948,0.0002056954,0.0006733221,0.0001993885,0.00002611246,0.0002484565,0.00007060726,0.0003772174,0.003243673,0.01670764,0.937368,0.04001087],"study_design_scores_gemma":[0.0000543326,0.0001415497,0.001025903,0.00003206477,0.00000612924,0.00004875636,0.00004441722,0.0003473135,0.001825053,0.001640459,0.9948196,0.0000144192],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01545354,0.003684444,0.0148928,0.01917722,0.02810691,0.001376148,0.0641499,0.01756739,0.8355916],"genre_scores_gemma":[0.04083166,0.0007218028,0.009088868,0.005836588,0.001552369,0.0009991998,0.04834164,0.003955234,0.8886726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1844776,"threshold_uncertainty_score":0.617139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03179318165978392,"score_gpt":0.2384113101170477,"score_spread":0.2066181284572638,"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."}}