{"id":"W4412084899","doi":"10.1364/ofc.2025.th1f.5","title":"Chiplet Solutions to Enable AI Scaling","year":2025,"lang":"en","type":"article","venue":"","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alpha Technologies (Canada)","funders":"","keywords":"Computer science; Scaling; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001224477,0.0000443663,0.00005111113,0.0001202619,0.0002510847,0.0002075665,0.0002417525,0.00002224993,0.0000139322],"category_scores_gemma":[0.00002773293,0.00003916971,0.00002105471,0.0004785883,0.00001431136,0.0002715491,0.0001325657,0.00005960957,0.00008795974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001839854,"about_ca_system_score_gemma":0.0001142962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003414273,"about_ca_topic_score_gemma":0.00000813208,"domain_scores_codex":[0.9995058,0.00001154358,0.00008551747,0.0001716129,0.00005756571,0.0001679793],"domain_scores_gemma":[0.9997085,0.00001359239,0.00001028428,0.0001815711,0.00005072129,0.00003527693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001498463,0.00003216255,0.0002806689,0.00002208846,0.00001034678,0.000001185838,0.0002921612,0.0006309851,0.00299376,0.3112911,0.03111811,0.6533259],"study_design_scores_gemma":[0.0005620344,0.0000432895,0.0008997538,0.000213697,0.00001495487,0.00006077296,0.0002235175,0.6049154,0.1076262,0.1542395,0.1306802,0.0005207536],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009362341,0.00005861249,0.9369587,0.00764507,0.0005812675,0.00003708808,1.64529e-7,0.0002098715,0.05357293],"genre_scores_gemma":[0.6601504,0.000002623764,0.3154829,0.005121813,0.00006468256,0.00001225943,5.359495e-7,0.000002856413,0.01916192],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6592141,"threshold_uncertainty_score":0.2001568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01015277818986984,"score_gpt":0.2571033700891528,"score_spread":0.246950591899283,"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."}}