{"id":"W4394251234","doi":"10.6084/m9.figshare.8262020","title":"Jerash Northwest Quarter GPR data","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Ground-penetrating radar; Geology; Geography; Archaeology; Computer science; Telecommunications; Radar","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007814518,0.001347051,0.000874994,0.002236807,0.0006345798,0.001325818,0.001863998,0.001293531,0.04767201],"category_scores_gemma":[0.002312887,0.0005618313,0.0007584326,0.003453167,0.0003166382,0.0007181099,0.001013703,0.0009777937,0.08501326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008645708,"about_ca_system_score_gemma":0.001769945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05945353,"about_ca_topic_score_gemma":0.08491436,"domain_scores_codex":[0.9992478,0.00009022577,0.0000557733,0.0001943277,0.0002840666,0.000127821],"domain_scores_gemma":[0.9988537,0.00008868006,0.00007120297,0.0003427221,0.0005469996,0.00009667473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001303488,0.00005824648,0.001186291,0.0003059628,0.0000303754,0.000046096,0.00004710182,0.001008116,0.000516683,0.0004294635,0.9891426,0.007098658],"study_design_scores_gemma":[0.0001563685,0.00002962962,0.01115556,0.0001363834,0.00002833471,0.00007499781,0.0001653205,0.00123588,0.001050061,0.0005546103,0.985371,0.00004185618],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008308549,0.00003999339,0.000178761,0.00005023462,0.00003432591,0.00002816923,0.9960856,0.0004728564,0.002279213],"genre_scores_gemma":[0.001207906,0.00003438344,0.0004699561,0.00002483812,0.000007058857,0.00005335085,0.9958038,0.00009418284,0.002304484],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05945353,"threshold_uncertainty_score":0.1594787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08433260207495367,"score_gpt":0.3081741445198082,"score_spread":0.2238415424448545,"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."}}