{"id":"W4402483999","doi":"10.1016/j.culher.2024.08.014","title":"Machine learning in analytical chemistry for cultural heritage: A comprehensive review","year":2024,"lang":"en","type":"review","venue":"Journal of Cultural Heritage","topic":"Cultural Heritage Materials Analysis","field":"Arts and Humanities","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Narodowe Centrum Nauki","keywords":"Cultural heritage; Engineering; Engineering ethics; Management science; Archaeology; History","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008008789,0.001044106,0.004933387,0.0001709802,0.0002251723,0.0009368873,0.0007654277,0.0003850273,0.002843002],"category_scores_gemma":[0.000410124,0.0005880903,0.003281942,0.0003494267,0.0002760618,0.0007181382,0.0001874427,0.002047725,0.0001201318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000522977,"about_ca_system_score_gemma":0.0001755766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000653612,"about_ca_topic_score_gemma":0.0000967809,"domain_scores_codex":[0.9948502,0.0004485655,0.00294011,0.0005516474,0.0005916129,0.0006178528],"domain_scores_gemma":[0.9966433,0.0002793526,0.0015987,0.0002803125,0.0009515117,0.0002468149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001517195,0.0003444534,0.000001671764,0.6982881,0.004039356,0.001714234,0.01026173,0.000008367498,0.00008985835,0.001894029,0.08798874,0.1952177],"study_design_scores_gemma":[0.0003389668,0.0001427122,1.623679e-7,0.06679601,0.004748556,0.000521987,0.003258343,0.00004570324,0.000001151773,0.00004952952,0.9234038,0.0006931004],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001253593,0.995303,5.716857e-7,0.0003774526,0.0007858322,0.0008021444,0.0003065108,0.00007031849,0.0022288],"genre_scores_gemma":[0.0001017989,0.974421,0.0001945654,0.0001608564,0.002427481,0.00007532551,0.0003933465,0.0001156602,0.02210994],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.835415,"threshold_uncertainty_score":0.999657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1170935972842597,"score_gpt":0.3627437917913747,"score_spread":0.245650194507115,"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."}}