{"id":"W4394053506","doi":"10.5281/zenodo.7388038","title":"Supplementary material for a usability evaluation of a semantic search for biological datasets","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Usability; Computer science; Semantic search; Information retrieval; World Wide Web; Semantic Web; Human–computer interaction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003440153,0.001575919,0.0006889842,0.003550952,0.0009076137,0.001637343,0.001036261,0.0009362543,0.1027901],"category_scores_gemma":[0.01399717,0.0003527391,0.0008273846,0.003255365,0.0003390218,0.001140504,0.001612383,0.0008684687,0.04625261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001533561,"about_ca_system_score_gemma":0.001257711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008638292,"about_ca_topic_score_gemma":0.02839031,"domain_scores_codex":[0.9972301,0.000867952,0.0004320419,0.0004804148,0.0008088736,0.0001806928],"domain_scores_gemma":[0.9884524,0.006829127,0.0003762932,0.001498868,0.0024036,0.0004398363],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003139017,0.0004917383,0.004132596,0.001457934,0.00007077646,0.0001185285,0.0001768324,0.000998897,0.0009209258,0.0009454413,0.9692096,0.02116273],"study_design_scores_gemma":[0.0007550103,0.0003226678,0.03264888,0.0005155088,0.0001074235,0.0005967775,0.0007431611,0.006920886,0.005353271,0.004650779,0.9472758,0.0001099928],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008163221,0.0002050843,0.003908751,0.0002729892,0.0001259554,0.0005098059,0.9744634,0.005812433,0.006538358],"genre_scores_gemma":[0.007776563,0.00006704216,0.008639811,0.0001998242,0.00001550227,0.001488902,0.9781939,0.0007252125,0.002893218],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9965599,"threshold_uncertainty_score":0.3438672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08542570881769637,"score_gpt":0.3367827754308616,"score_spread":0.2513570666131653,"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."}}