{"id":"W4396891802","doi":"10.1364/ofc.2024.tu2a.3","title":"Liquid Cooling for Optical Networking Equipment","year":2024,"lang":"en","type":"article","venue":"","topic":"Semiconductor Lasers and Optical Devices","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ciena (Canada)","funders":"","keywords":"Computer cooling; Computer science; Networking hardware; Optical networking; Optoelectronics; Materials science; Wavelength-division multiplexing; Computer network; Thermal management of electronic devices and systems; Engineering; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007892902,0.00008323583,0.00008604451,0.00002844168,0.0000224702,0.00009110519,0.00005758485,0.00004731019,0.0001135332],"category_scores_gemma":[0.000005869278,0.00006818474,0.0000555102,0.00007397352,0.00001056236,0.00007159287,0.00001562322,0.00007255949,0.00005666132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003062103,"about_ca_system_score_gemma":0.000006180457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001176394,"about_ca_topic_score_gemma":0.00000217786,"domain_scores_codex":[0.9994758,0.000001247585,0.0001191327,0.0001166472,0.00006011779,0.0002270521],"domain_scores_gemma":[0.9997143,0.0001412041,0.00000194992,0.00006637102,0.000009509296,0.00006671129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001688836,0.00005892184,0.00004522548,0.002638086,0.0007372628,0.0001158731,0.0006469204,0.1110521,0.2239885,0.4866389,0.07162246,0.1022869],"study_design_scores_gemma":[0.0000828194,0.00006175515,0.000005098853,0.000112894,0.00002496269,0.000002977186,0.0000402735,0.7822615,0.01923663,0.0003426151,0.1976557,0.0001727939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6540733,0.01548232,0.2191918,0.0004624724,0.01016478,0.0005820583,0.00001057937,0.003470534,0.09656213],"genre_scores_gemma":[0.9940712,0.00007735342,0.004656745,0.0001114588,0.0007743613,0.00002565009,0.000005749358,0.00003084269,0.0002466137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6712095,"threshold_uncertainty_score":0.2780493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02090155192951458,"score_gpt":0.2526735566053154,"score_spread":0.2317720046758008,"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."}}