{"id":"W6950351120","doi":"10.5281/zenodo.7117696","title":"Open Access als Einnahmequelle. Wie die großen Wissenschaftsverlage von Publikationsgebühren profitieren","year":2022,"lang":"de","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Liberian dollar; Us dollar","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["scholarly_communication"],"domain":null,"study_design":"not_applicable","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["scholarly_communication"],"domain":null,"study_design":"theoretical_or_conceptual","genre":"commentary","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.002367375,0.0004095964,0.0005297691,0.0006649876,0.00707241,0.00676198,0.0062672,0.0001469974,0.06193364],"category_scores_gemma":[0.004207154,0.0004614875,0.0001382985,0.001673003,0.0003111424,0.001772624,0.02675757,0.00124745,0.01231007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006616169,"about_ca_system_score_gemma":0.00006830361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004452151,"about_ca_topic_score_gemma":0.000001925443,"domain_scores_codex":[0.9946165,0.001239315,0.0007286308,0.00108649,0.001333123,0.0009959519],"domain_scores_gemma":[0.9963852,0.0001886139,0.0004327288,0.001323802,0.0009548503,0.0007147965],"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":[0.0008530355,0.001002428,0.0001931578,0.0002800422,0.0005697933,0.0004289838,0.001819751,0.000302457,0.0006624978,0.009924424,0.8804001,0.1035633],"study_design_scores_gemma":[0.002422059,0.001140154,0.002483904,0.0001549669,0.0002748771,0.000114718,0.0010274,0.0009587528,0.0004741576,0.0007044036,0.9897211,0.0005234653],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0493405,0.0169342,0.004999931,0.03783819,0.003558122,0.01230308,0.01288008,0.004360378,0.8577855],"genre_scores_gemma":[0.9402679,0.001684845,0.0003859344,0.001775842,0.001347102,0.000002289815,0.03332828,0.004015902,0.01719187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8909274,"threshold_uncertainty_score":0.9997837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09517664956324053,"score_gpt":0.3324358485123127,"score_spread":0.2372591989490722,"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."}}