{"id":"W1483930064","doi":"10.22230/src.2013v4n3a123","title":"Reading Environments for Genetic Editions","year":2013,"lang":"en","type":"article","venue":"Scholarly and Research Communication","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reading (process); Computer science; Presentation (obstetrics); Digital humanities; Philology; Set (abstract data type); World Wide Web; Documentation; State (computer science); Close reading; Linguistics; Literature; Art; Sociology; Philosophy; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005344384,0.0007341421,0.0004380675,0.001929488,0.002373622,0.01008369,0.001689456,0.001495761,0.02934802],"category_scores_gemma":[0.02952732,0.0004690299,0.0005020519,0.001395985,0.002374761,0.007785216,0.007703684,0.001321507,0.007731513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006470538,"about_ca_system_score_gemma":0.0007577622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001733325,"about_ca_topic_score_gemma":0.0003412204,"domain_scores_codex":[0.995827,0.002194765,0.0002643917,0.000534441,0.0009749109,0.0002044891],"domain_scores_gemma":[0.9708029,0.02197905,0.001115222,0.003646471,0.001304755,0.001151538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008770917,0.0004398455,0.005458476,0.0009673677,0.00003496803,0.003874759,0.1418849,0.001665539,0.01465916,0.1996155,0.06556951,0.5649529],"study_design_scores_gemma":[0.0001485871,0.0003250217,0.003235542,0.0005119201,0.00005802641,0.002163167,0.01831628,0.0025093,0.009139547,0.06802565,0.8954319,0.0001350604],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2673292,0.002940982,0.3784373,0.007234028,0.002241524,0.0007004462,0.00124356,0.0330138,0.3068591],"genre_scores_gemma":[0.6341138,0.001674609,0.259445,0.001452307,0.0009677506,0.0008116167,0.00141799,0.00683583,0.09328104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9899163,"threshold_uncertainty_score":0.09817892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1471699120763154,"score_gpt":0.3276753681374433,"score_spread":0.1805054560611279,"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."}}