{"id":"W6944036826","doi":"10.17630/80f522b6-6d23-4751-9023-21a1e3d0eb5a","title":"Instrumented Digital and Paper Reading (dataset)","year":2019,"lang":"en","type":"dataset","venue":"University of St Andrews","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Readability; Python (programming language); Histogram; Paragraph; Annotation; Reading (process); Feature (linguistics); Classifier (UML); Image processing","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009831045,0.0002818645,0.0004698064,0.0003207251,0.0001234647,0.00006070292,0.0004779865,0.0002811031,0.001196442],"category_scores_gemma":[0.00002668646,0.0003351134,0.00007766433,0.0001700865,0.0002922314,0.0008020555,0.0005898735,0.0003231233,0.002473939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001009871,"about_ca_system_score_gemma":0.00007333846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007863839,"about_ca_topic_score_gemma":0.0002548458,"domain_scores_codex":[0.9988217,0.00003311785,0.0001535786,0.0004425881,0.0002930823,0.0002559473],"domain_scores_gemma":[0.9988676,0.00004295403,0.0003040268,0.0005999492,0.00005208851,0.0001333434],"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":[0.0001758474,0.00006101546,0.0002014799,0.000122437,0.0001580788,0.00004700394,0.0000443653,3.832492e-7,0.0000681384,0.000004451664,0.9987409,0.0003758962],"study_design_scores_gemma":[0.0009922038,0.00009012502,0.0001481193,0.0001579957,0.0002160934,0.00001102087,0.0005035871,0.000006391475,0.000005069592,0.00000553873,0.9975338,0.0003300489],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002339875,0.00004367926,0.000001191527,0.00002172795,0.00008638584,0.0002275573,0.9966992,0.00002368868,0.0005566583],"genre_scores_gemma":[0.0004219309,0.0003275976,0.00003001174,0.00002769973,0.00002251706,3.552289e-8,0.9985243,0.00002133355,0.0006245807],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.001917944,"threshold_uncertainty_score":0.9999101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01307567003390726,"score_gpt":0.2050008742510687,"score_spread":0.1919252042171615,"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."}}