An Open-Source Archiving System
Bibliographic record
Abstract
Establishment of a Web-based repository for wireless multipath channel measurement data would make it possible for the researchers to pool their datasets to yield more reliable or broadly applicable results or to extract additional value from data that may have been collected for other purposes. It would also allow the validity of models derived from certain data sets to be more easily tested against other data sets than at present. Moreover, ongoing developments in Web technologies will greatly add to the capabilities of such repositories in the coming years. While the wireless community would greatly benefit from the establishment of such a repository for channel measurement data, account must be taken of the significant differences between wireless channel-response data and data collected in other scientific fields. A particu ar challenge is to ensure that essential details concerning the measurement equipment used to collect the data, the manner in which the equipment was calibrated and verified, the data collection procedure, and details of the environment in which the data were collected are adequately documented and linked to the channel response data. Nevertheless, it seems likely that the return from such a wireless channel-response data repository would justify the effort required to set up and maintain it.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.165 | 0.244 |
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".