{"id":"W4408655628","doi":"10.1117/12.3040780","title":"Energy efficient optical interconnects for the next generation of AI compute","year":2025,"lang":"en","type":"article","venue":"","topic":"Semiconductor Lasers and Optical Devices","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cenovus Energy (Canada)","funders":"","keywords":"Computer science; Energy (signal processing); Electronic engineering; Physics; Engineering","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":[0.0003172595,0.0003285664,0.0001794672,0.0006133278,0.0008480258,0.001431446,0.0007214229,0.0007196564,0.007470759],"category_scores_gemma":[0.0007293351,0.0001925447,0.0001743487,0.0006538872,0.0005210713,0.002736252,0.0006328917,0.0007155481,0.001537848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001124497,"about_ca_system_score_gemma":0.0005820512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007279966,"about_ca_topic_score_gemma":0.002261019,"domain_scores_codex":[0.9998237,0.00001977655,0.000005685232,0.00001827449,0.0001002845,0.00003223688],"domain_scores_gemma":[0.9996198,0.00009680084,0.00007035618,0.00004559011,0.0001383697,0.00002904759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002428387,0.0002224042,0.001079863,0.0005831833,0.00003693289,0.0003528447,0.0002897551,0.03285747,0.1795651,0.5466518,0.04018373,0.197934],"study_design_scores_gemma":[0.00005206568,0.0005258091,0.001355979,0.0003216051,0.0000635115,0.0006121058,0.0005631242,0.2322917,0.1040191,0.1955015,0.4645849,0.0001085396],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1501974,0.03412361,0.438196,0.0207684,0.003591714,0.0002058775,0.0005975017,0.003199471,0.3491201],"genre_scores_gemma":[0.7735943,0.01508707,0.147869,0.001985818,0.001136667,0.0001658496,0.0003708053,0.0003046553,0.05948587],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007470759,"threshold_uncertainty_score":0.02499217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03559881496076265,"score_gpt":0.2498438180150334,"score_spread":0.2142450030542708,"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."}}