{"id":"W6893132917","doi":"10.5281/zenodo.14710297","title":"INSTAR - QR cards","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"QR Code Applications and Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Key (lock); Information technology; Digital economy; Emerging technologies","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002744117,0.00007892775,0.00008087089,0.0002521252,0.001395907,0.0007394238,0.0021785,0.0000490303,0.0004841623],"category_scores_gemma":[0.0001942493,0.00008033931,0.00003142457,0.001076423,0.00009820131,0.0002513659,0.002228031,0.0001558854,0.00230746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007936417,"about_ca_system_score_gemma":0.000005893487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001086969,"about_ca_topic_score_gemma":1.614468e-7,"domain_scores_codex":[0.9990868,0.00006664504,0.0001365421,0.0003296874,0.0001662203,0.0002140841],"domain_scores_gemma":[0.9988158,0.00001338377,0.00003652689,0.0007976132,0.0002909157,0.00004575678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000211581,0.00003902627,0.000002561159,0.00001078206,0.00001281036,0.000002319391,0.0001034597,0.0000112843,0.001148944,0.5548301,0.106815,0.3370216],"study_design_scores_gemma":[0.000144008,0.00004094352,0.0004576778,0.000009305502,0.000002771644,0.00001283084,0.00006776817,0.001185105,0.001202408,0.004269248,0.9925254,0.00008254858],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006190817,0.0001933447,0.5385432,0.005376658,0.0001036838,0.0003270007,0.00002845423,0.003616038,0.4456207],"genre_scores_gemma":[0.9908509,0.00008456254,0.006791182,0.0003210681,0.00002588811,9.814848e-8,0.0001004193,0.0002443837,0.001581506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9846601,"threshold_uncertainty_score":0.9999042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02366836551670544,"score_gpt":0.2453922726711087,"score_spread":0.2217239071544033,"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."}}