{"id":"W4383219807","doi":"10.1145/3607479.3607481","title":"SIGCSE Technical Symposium 2023 Report","year":2023,"lang":"en","type":"article","venue":"ACM SIGCSE Bulletin","topic":"Academic Publishing and Open Access","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Attendance; Library science; Coronavirus disease 2019 (COVID-19); Computer science; Political science; Medicine; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.01274003,0.0002957231,0.000528186,0.0005586345,0.0003771086,0.000949838,0.006922164,0.0004523604,0.004404722],"category_scores_gemma":[0.07332823,0.0002238808,0.0002548475,0.003086268,0.0002261939,0.0005062319,0.003161722,0.00090968,0.03680918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007281592,"about_ca_system_score_gemma":0.0001667063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001599492,"about_ca_topic_score_gemma":0.0000133785,"domain_scores_codex":[0.9932687,0.0003271688,0.001428046,0.001323502,0.002855221,0.0007973266],"domain_scores_gemma":[0.9907292,0.004977428,0.0005346083,0.00297029,0.0003947812,0.0003937256],"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":[0.00002414794,0.0000302134,0.004737963,0.000002654605,0.000009931289,0.0007423078,0.00007634145,0.00008514889,0.0006888226,0.0001787379,0.9866366,0.006787079],"study_design_scores_gemma":[0.0002909637,0.00003668479,0.007893657,0.00002413034,0.00001271345,0.0002826913,0.0003197908,0.00008339064,0.0008587777,0.01229374,0.977569,0.0003344285],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2816531,0.0003544604,0.0008094325,0.4324836,0.004513299,0.0009868101,0.00007595775,0.002202552,0.2769208],"genre_scores_gemma":[0.6913458,0.0001737734,0.001552389,0.003381808,0.001073789,0.0001353879,0.00007789338,0.00007199679,0.3021872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4291018,"threshold_uncertainty_score":0.9984509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08576458313553763,"score_gpt":0.3997910284691278,"score_spread":0.3140264453335902,"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."}}