{"id":"W2895460691","doi":"10.32920/24132891","title":"Integrity Proofs for RDF Graphs","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; RDF; Merkle tree; Mathematical proof; Data integrity; Hash function; Construct (python library); SPARQL; Theoretical computer science; Data mining; Information retrieval; Semantic Web; Database; Programming language; Cryptographic hash function; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01074284,0.0002145318,0.0004465547,0.0004478187,0.0001144026,0.0008535793,0.0025876,0.0002342958,0.0006887483],"category_scores_gemma":[0.005828421,0.0001452602,0.0003835145,0.0004281459,0.00008796168,0.0001642757,0.004217827,0.0004665651,0.001920269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003255473,"about_ca_system_score_gemma":0.0001105482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004380874,"about_ca_topic_score_gemma":0.001242207,"domain_scores_codex":[0.9962247,0.0001827882,0.0008535531,0.001128696,0.001316357,0.0002938886],"domain_scores_gemma":[0.9957849,0.001566794,0.0002939952,0.001892729,0.000359249,0.0001022836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001415687,0.00004532096,0.0001270756,0.00007096891,0.0000389849,0.000001765975,0.0001228053,0.00003019428,9.94728e-7,0.2548106,0.6956071,0.04913003],"study_design_scores_gemma":[0.00008949544,0.00001965574,0.0009013867,0.0000215866,0.00001419992,1.131288e-7,0.0005038083,0.0007904826,0.0000305279,0.6678297,0.329653,0.0001460779],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.003361673,0.00008643015,0.8705301,0.03553652,0.01028771,0.004501631,0.002245958,0.0009790958,0.0724709],"genre_scores_gemma":[0.1072121,0.0002096846,0.09281871,0.006285324,0.0007444348,0.002672247,0.001724096,0.0001108967,0.7882226],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7777114,"threshold_uncertainty_score":0.9988568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.660765239254517,"score_gpt":0.5361311141273202,"score_spread":0.1246341251271968,"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."}}