{"id":"W4406262080","doi":"10.1109/qce60285.2024.10416","title":"piQture: A QML Library for Image Processing","year":2024,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Image processing; Image (mathematics); Computer graphics (images); Artificial intelligence; Computer vision; Arithmetic; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00109705,0.001516214,0.001083742,0.001229762,0.0008163572,0.002016173,0.005664483,0.001456278,0.07198934],"category_scores_gemma":[0.004628602,0.001353726,0.00201447,0.001367042,0.001091053,0.00401075,0.003507975,0.004287581,0.04043707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095694,"about_ca_system_score_gemma":0.002320646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003147849,"about_ca_topic_score_gemma":0.004896185,"domain_scores_codex":[0.9992883,0.00009481682,0.00005890589,0.0001395446,0.0003303614,0.00008819123],"domain_scores_gemma":[0.9990082,0.0003359882,0.00007430117,0.0002591501,0.000234545,0.00008779264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005160187,0.0002147116,0.001739355,0.00258803,0.0002222588,0.0003648385,0.0003808753,0.02405058,0.01614967,0.07777147,0.6898535,0.1861487],"study_design_scores_gemma":[0.0002736981,0.0001143981,0.001394736,0.0002823263,0.00006547481,0.0004551031,0.00007611678,0.3191589,0.04834011,0.1195008,0.5101174,0.0002209735],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001257219,0.0002472563,0.6402964,0.0003752092,0.0001499004,0.000143386,0.01333986,0.3363658,0.007824974],"genre_scores_gemma":[0.052612,0.001071667,0.7087508,0.001787321,0.0001811825,0.001763555,0.05400209,0.1557003,0.02413092],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.07198934,"threshold_uncertainty_score":0.2408283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160400959190227,"score_gpt":0.2750151691091527,"score_spread":0.25897507319013,"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."}}