Bibliographic record
Abstract
56 additive white Gaussian noise channel, 65 admission control, 8 ADSL, See asynchronous digital subscriber links ALOHA, 12, 207 antithetic variate technique, 384 application layer, 8 ARIMA.See arithmetic moving averagen arithmetic average, 360 Arithmetic Moving Averages, 305 ARPANET, 5 ARQ, 64, 164 asynchronous digital subscriber links, 3 asynchronous time division multiplexing, 190 asynchronous transfer mode, See ATM ATDM, 190 ATM , 35, 68, 138, 188, 196 ATM cell, 5, 68, 188 autocorrelation function, 41, 62, 283 autocovariance function, 41, 283 automatic-repeat-request, See ARQ axioms of probability, 20 bandwidth, 303 batch Markov arrival process, 316 Bayes' rule, 21 Bell, A.G., 11 Bernoulli distribution, 27 Bernoulli trials, 26, 52, 106 binary symmetric channel, 21 binomial distribution, 26, 36, 37, 377 binomial theorem, 43 birth and death process, 12, 63, 74, 82, 84, 85, 94, 108, 116, 284, 287 blocked calls cleared, 96 blocked calls held, 93 Blood, G.T., 11 Bloomsday, 310 buffer insertion rings, 261 Burke's theorem, 118, 120 bursty data sources, 190 busy period, 234, 237 cable modem, 3 cable television, 3 call second, 87 call setup time, 5 capacity allocation, 137, 138 389 Modeling and Analysis of Telecommunications Networks, by Jeremiah F. Hayes and Thimma V.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.585 | 0.576 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".