{"id":"W3099545975","doi":"","title":"“Within the giant’s belly”. Multi-method approach of the long barrows of Tusson (Charente, France)","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Metallurgy and Cultural Artifacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Heritage","funders":"","keywords":"Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0130931,0.0002502556,0.0004161345,0.00005752184,0.000946739,0.0001363083,0.002621175,0.0003194606,0.0003480338],"category_scores_gemma":[0.003301387,0.0001562671,0.0003683562,0.000510229,0.001264435,0.0001226022,0.001212984,0.0006572678,0.00002999296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005214357,"about_ca_system_score_gemma":0.0003896838,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00708283,"about_ca_topic_score_gemma":0.01018901,"domain_scores_codex":[0.9857865,0.01191646,0.0006939342,0.000523277,0.0007698009,0.0003100395],"domain_scores_gemma":[0.9939417,0.0008380408,0.001253026,0.001832115,0.002015083,0.0001200224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004996688,0.002427781,0.01667004,0.0009566478,0.0005614441,0.000001855736,0.5729337,0.0009826291,0.01430249,0.2834805,0.001192633,0.1064404],"study_design_scores_gemma":[0.002594684,0.000003957199,0.1845489,0.01190226,0.0007110273,0.00001615021,0.01917333,0.09495497,0.6043348,0.02075957,0.05849925,0.002501171],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5337101,0.002647236,0.2407311,0.0270866,0.00222543,0.003358338,0.00008596155,0.0002517194,0.1899036],"genre_scores_gemma":[0.9411575,0.000201728,0.03741967,0.00008645973,0.00004919299,0.00004598661,0.00004449143,0.00002181273,0.0209732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5900323,"threshold_uncertainty_score":0.9995291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03248876892272781,"score_gpt":0.2819933321820511,"score_spread":0.2495045632593233,"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."}}