Bibliographic record
Abstract
White Space refers to the unused broadcasting frequencies in the wireless spectrum. Television networks leave gaps between channels for buffering purposes, and this space in the wireless spectrum is similar to what is used for 4G and so it can be used to deliver widespread broadband internet. Typical home Wi-Fi can travel through two walls. WhiteSpace broadband can travel up to 10 kilometers, through vegetation, buildings, and other obstacles. Tablets, phones, and computers can all access this wireless internet using White Space through fixed or portable power stations. The actual amounts of spectrum vary by region, but White Space spectrum ranges from 470 MHz to 790 Mhz. So does your laptop have the hardware to connect straight to the broadband spectrum? Not yet, according to Alan Stillwell, incentive auctions advisor for the FCC's Office of Engineering and Technology (OET). "If you want to use TVWS (TV White Space) with your existing laptop, you would need a separate device," Stillwell said. "The TVWS devices in use right not do not serve computers (towers, laptops or tablets) directly. They provide a link to a receiver that is connected to a regular WiFi hub.” One of the largest white space technology trials occurred in Cambridge in 2011 when Microsoft, the BBC, BT, and Nokia launched a consortium to support the project. Then, at the end of last year, Ofcom, the regulator of UK communications industries, announced a six month trial with 20 private and public sector organizations, making it Europe's first major pilot of the technology. There have been other successful tests in Canada and Africa in recent years, as well. In 2011, Wilmington, North Carolina implemented White Space technology to connect the city's infrastructure, allowing public officials to remotely turn lights on and off in parks, provide public wireless broadband to certain areas of the city, and monitor water levels. At West Virginia University, White Space technology is used to power a "super Wi-Fi network". It started in 2013 with wireless internet on the campus public transit platform, which transports about 15,000 students a day. WVU is the first campus to utilize White Space broadband internet.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.145 | 0.091 |
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".