{"id":"W4414359804","doi":"10.24963/ijcai.2025/475","title":"Adaptive Deep Learning from Crowds","year":2025,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Crowds; Crowdsourcing; Flexibility (engineering); Probabilistic logic; Focus (optics); Deep learning; Annotation; Adaptive learning; Reduction (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":[0.001994778,0.001733951,0.001734049,0.0009012427,0.0009108507,0.001075035,0.003362745,0.002015039,0.002583524],"category_scores_gemma":[0.005135916,0.0008127131,0.0009393507,0.001052298,0.001851195,0.002051042,0.003346135,0.002190702,0.0007607105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002085738,"about_ca_system_score_gemma":0.00206833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01017711,"about_ca_topic_score_gemma":0.01113161,"domain_scores_codex":[0.9987551,0.0003266859,0.00004329001,0.0004640571,0.0002240035,0.0001869113],"domain_scores_gemma":[0.9981886,0.0009352154,0.0001577033,0.000276039,0.0002930536,0.0001494853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003046333,0.0001656119,0.001926607,0.0001719393,0.00009892842,0.0001271167,0.0002008641,0.8147658,0.002650633,0.01048545,0.006867666,0.1622348],"study_design_scores_gemma":[0.00001145431,0.00001779155,0.00009957094,0.000007850219,0.000005623304,0.000009782548,0.00001499249,0.9854805,0.0005752832,0.01318477,0.0005869509,0.00000543774],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04705362,0.0008440381,0.94402,0.001148186,0.0001554719,0.0001209923,0.0003861021,0.002407215,0.00386438],"genre_scores_gemma":[0.8275021,0.0004205723,0.1601551,0.001173751,0.0001982255,0.0003248897,0.001084721,0.0002504322,0.008890289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01017711,"threshold_uncertainty_score":0.02023572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01035939629332343,"score_gpt":0.2206287167555865,"score_spread":0.2102693204622631,"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."}}