{"id":"W7048736821","doi":"","title":"Milne en masse: A Case Study in Digitizing Large Image Collections","year":2016,"lang":"en","type":"article","venue":"","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Photography; Digital photography; Key (lock); Computational photography; Digital image; Order (exchange)","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":[0.0101494,0.0006344519,0.0005262112,0.004653599,0.02140484,0.0123795,0.004264037,0.005060308,0.008946178],"category_scores_gemma":[0.02512126,0.0008793091,0.0005020909,0.008078263,0.008962567,0.008941215,0.009946895,0.004017764,0.001579921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0101435,"about_ca_system_score_gemma":0.004874157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05781833,"about_ca_topic_score_gemma":0.1876621,"domain_scores_codex":[0.9931466,0.003014744,0.0003790671,0.000855698,0.001861469,0.0007423193],"domain_scores_gemma":[0.9866716,0.007632256,0.001153871,0.002128788,0.001506239,0.0009072535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009370053,0.0002974675,0.01450857,0.0007471038,0.00004212155,0.0437208,0.8031826,0.0006806631,0.002147751,0.0325382,0.02961004,0.07243096],"study_design_scores_gemma":[0.000008009452,0.00004853425,0.009326164,0.0008531892,0.00002688669,0.01418116,0.6476256,0.0005487964,0.001592371,0.002917628,0.3228134,0.00005816183],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7719052,0.005812043,0.01256523,0.02324122,0.0003777226,0.001053356,0.0008057408,0.0003323104,0.1839071],"genre_scores_gemma":[0.9042704,0.00551645,0.02250915,0.003989282,0.0001712418,0.000546975,0.0005334002,0.0004769609,0.06198614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05781833,"threshold_uncertainty_score":0.1149636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185582044313327,"score_gpt":0.3015064171891823,"score_spread":0.289650596746049,"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."}}