{"id":"W6912774644","doi":"10.5281/zenodo.3551791","title":"Small, thick, and slow: Thinking about data and research publication in the Humanities in the age of Open and FAIR","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Relation (database); Value (mathematics); Function (biology); Point (geometry); Citation; Core (optical fiber); Statistician","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":["scholarly_communication","open_science"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.005498608,0.00005870064,0.0000812905,0.0001766262,0.0001680106,0.01037146,0.007294782,0.0000283277,0.0002418056],"category_scores_gemma":[0.002886873,0.00003620431,0.000003607464,0.0004376284,0.0000439529,0.01599794,0.009831485,0.0003585037,0.00001614126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008305533,"about_ca_system_score_gemma":0.00005237815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004337589,"about_ca_topic_score_gemma":0.001071545,"domain_scores_codex":[0.9981056,0.0007004473,0.0001364775,0.0003757131,0.0004909566,0.000190793],"domain_scores_gemma":[0.9966277,0.00171448,0.00006830342,0.001497884,0.00007938273,0.00001217822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004615374,0.0002585549,0.02054799,0.002101378,0.00004671684,0.000124769,0.06912314,0.00000309284,0.0001224414,0.4630633,0.4009919,0.04357054],"study_design_scores_gemma":[0.0004064113,0.0000935207,0.3748393,0.0006886083,0.000001530789,0.000007669362,0.004848456,0.004394671,0.000009913432,0.0032748,0.6113183,0.0001168095],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2748094,0.014808,0.0003387415,0.1656753,0.0001552608,0.02534368,0.07858369,0.000181494,0.4401044],"genre_scores_gemma":[0.9739117,0.0006192123,0.001402599,0.002044247,0.00004320622,0.0003398741,0.02053145,0.00001296479,0.001094772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6991022,"threshold_uncertainty_score":0.9981768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5648925420130165,"score_gpt":0.4527862992005512,"score_spread":0.1121062428124653,"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."}}