Bibliographic record
Abstract
additive white Gaussian noise (AWGN) channel, 335 alternate mark inversion (AMI), 139, 140, 169-72, 170-172 Ampere's circuital law, 4, 4-5 Ampere's Law Ampere's circuital law, 4, 4-5 density, 4, 5 differential form, 9 magnetic field intensity, 3-6, 4 magnetic flux, 4, 5-6 permeability, 5 relative permeability, 5 amplified spontaneous emission (ASE) amplifier noise figure, 260, 260-262 amplifier spacing impact, 318-19, 319, 323, 326-7, 326-7 ASE-ASE beat noise, 253, 256, 259, 261, 291-6, 292, 295, 323, 471 bandwidth, 252, 252, 262, 290, 292, 294 cutoff frequency, 255, 292, 294 dark current, 260 in EDFAs, 280-281 electrical filter, 254-5, 288-96, 292, 295 equivalent noise figure, 317, 317-18 field envelope, 251-2, 252 mean noise power, 254-5 mean (total), 256-8 noise, band-pass representation, 250, 250 Fiber Optic Communications: Fundamentals and Applications, First Edition.Shiva Kumar and M.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.533 | 0.476 |
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".