Bibliographic record
Abstract
Testing is essential for both the radio aspects and the protocol aspects of TETRA mobile and base station radio equipment. Different types of testing are required at the stages of R&D, conformance/type approval, manufacturing/commissioning and servicing. Testing for radio aspects has presented tough design challenges to TETRA equipment manufacturers and to test equipment manufacturers in achieving the required performance. Notable challenges are high dynamic range, low adjacent channel power, and accurate implementation of TETRA filtering. The radio conformance specifications, and the test equipment required, continue to evolve. Future radio testing requirements will include VHF operation, extended range, air-to-ground operation and on-channel repeaters. Testing for protocol aspects is of particular importance at the stages of R&D and conformance/type approval, since it is the software design being tested. TETRA radios include configuration for the intended user, group and network, which is confirmed using functional testing. Protocol conformance testing is specified using formal conformance test cases, which give a high degree of confidence but are time consuming to develop. A pragmatic solution at present is interoperability testing (IOP), in which mobile, infrastructure and test equipment from different manufacturers is tested against each other. (19 pages)
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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".