{"id":"W2078836978","doi":"10.1016/j.jas.2012.06.013","title":"Multispectral images of ostraca: acquisition and analysis","year":2012,"lang":"en","type":"article","venue":"Journal of Archaeological Science","topic":"Cultural Heritage Materials Analysis","field":"Arts and Humanities","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Tel Aviv University; Azrieli Foundation; Israel Science Foundation","keywords":"Multispectral image; Readability; Brightness; Contrast (vision); Computer science; Quality (philosophy); Multispectral pattern recognition; Semitic languages; Artificial intelligence; Remote sensing; Geography; Optics; Arabic; Physics; Linguistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0001649872,0.0002305498,0.0001742352,0.001855313,0.0005012209,0.0005796236,0.0002733759,0.000405242,0.002569382],"category_scores_gemma":[0.0003382953,0.000242779,0.0002263558,0.001623286,0.0002453931,0.0003942425,0.000455844,0.0002565777,0.0007320421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003357849,"about_ca_system_score_gemma":0.0006963733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01755193,"about_ca_topic_score_gemma":0.06356143,"domain_scores_codex":[0.9998158,0.00001004248,0.000008560814,0.00003828354,0.00008943087,0.00003799094],"domain_scores_gemma":[0.9997581,0.00002311196,0.00002181606,0.00003833207,0.0001359118,0.0000227945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006566317,0.0001519691,0.05417025,0.0003371206,0.00009228069,0.0009857314,0.001602744,0.005107916,0.7504301,0.0008782427,0.004144537,0.1814424],"study_design_scores_gemma":[0.0000261369,0.0001001971,0.9019188,0.00005908545,0.0001568317,0.002022297,0.001025592,0.01511981,0.06310903,0.00024709,0.01616916,0.00004596294],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759349,0.0003448262,0.008619176,0.0001142719,0.00002016015,0.00009467837,0.002615951,0.0001937176,0.01206244],"genre_scores_gemma":[0.9529297,0.0004161336,0.03772036,0.00004176777,0.0000219543,0.00006372535,0.002262771,0.000181062,0.006362495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01755193,"threshold_uncertainty_score":0.03489953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03060076445715754,"score_gpt":0.2672764830849146,"score_spread":0.2366757186277571,"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."}}