Whitespace evaluation software (WEST) and its applications to whitespace in Canada and Australia
Bibliographic record
Abstract
Spectrum whitespaces and dynamic spectrum sharing have become important and interesting topics in recent years. The USA authorized the use of TV whitespaces in 2008 and the UK and Canada followed suit in early 2015. In light of the PCAST report of 2012, additional bands are being evaluated for spectrum sharing in the USA and abroad. With the increasing momentum of spectrum whitespaces, it is more important than ever to understand the consequences of regulatory decisions. For example, what is the effect of increasing the separation distance from 10km to 15km? Regulators need the ability to understand tradeoffs like this so that they can make informed decisions based on actual, not hypothetical or supposed, impact. Despite the clear need, data-driven analyses appear to be quite rare among regulators, industry members, and researchers alike. Although the data is often freely available, employing it can be an onerous task. In order to reduce this barrier, we have created an open-source software package, WEST, that quickly allows a user to estimate the amount of whitespace in a given region. For example, after collecting the requisite data, we produced estimates of the amount of whitespace in Canada in under an hour. To demonstrate the power of our software, we present novel results on whitespace availability in Canada and Australia. However, the true potential of WEST lies in the ability to configure it to use existing or hypothetical rulesets. We thus use WEST to compare the FCC and Industry Canada (IC) rulesets, showing that each citizen loses approximately one whitespace channel, mainly due to the increased size of IC's separation distances as compared to the FCC's. We also showed that although the effect of taboo channel exclusions (a notion introduced in the IC ruleset) is small in Canada, it would be much larger if applied to the USA. The identification of the real-world effects of these regulatory decisions was made possible by WEST's ability to create “chimera rulesets,” i.e. mosaics of the IC and FCC rules, so that we could examine each variable in isolation. Finally, we describe the high-level design of WEST. The modular design makes it easy for users to combine, replace, modify, or remove various components to achieve the desired effect. We sincerely hope that the community will use and contribute to WEST, turning it into an even more powerful tool than it is today. If real-world data were at your fingertips and easy to use, what would you do?
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".