{"id":"W4393886955","doi":"10.5281/zenodo.3581648","title":"HGIS data of Greater London's Industry 1865-1875","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Historical and Cultural Archaeology Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Business; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005864783,0.00097441,0.0008049219,0.005928143,0.0006497512,0.002125304,0.001482456,0.0009340121,0.0386395],"category_scores_gemma":[0.003950635,0.0005132508,0.0005508801,0.01302446,0.0004140417,0.0009184399,0.001784083,0.001133674,0.05255476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00263027,"about_ca_system_score_gemma":0.003333973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1221698,"about_ca_topic_score_gemma":0.2401537,"domain_scores_codex":[0.999014,0.00009638303,0.0001491226,0.000253601,0.0002983255,0.0001885939],"domain_scores_gemma":[0.9974184,0.0003364346,0.0005892807,0.000400651,0.0009721797,0.0002830891],"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":[0.00006064557,0.00001776535,0.005487217,0.0005481202,0.00003407762,0.00006891174,0.0001586202,0.0001847226,0.0001184561,0.0011432,0.9893225,0.002855731],"study_design_scores_gemma":[0.00004545603,0.000007867626,0.02649083,0.0002596267,0.00001387982,0.00005036844,0.0004807375,0.00008386685,0.0002192592,0.0002616732,0.9720696,0.00001684005],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007407292,0.0000834989,0.0000337829,0.00005502538,0.00001978406,0.000008293,0.9976162,0.00004343134,0.001399256],"genre_scores_gemma":[0.001126937,0.00006962253,0.0001114905,0.00002750025,0.000008788174,0.000057033,0.9962407,0.00002389468,0.002333975],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1221698,"threshold_uncertainty_score":0.2429174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.123678564135847,"score_gpt":0.3168311846642236,"score_spread":0.1931526205283765,"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."}}