In this issue of <i>Weather</i>
Bibliographic record
Abstract
We begin our February 2016 issue of Weather with a study of the dangers of winds over mountain ranges on p. 27. In ‘Wind hazard in the alpine zone: a case study in Alberta, Canada’ Chris Hugenholtz and Geoffrey Van Heller discuss the effects of winds of a relatively extreme mountain environment using data from an automatic weather station in the Front Ranges of the eastern Rocky Mountains. Dangers may include the buffeting and chilling effects of the wind itself, the melting of snow due to adiabatic warming and the formation of intense areas of low pressure in the lee of the mountains. Economic losses may be significant, and there is a clear need for all to be aware of the effects of strong winds over mountains. On p. 32, the next paper is an interesting study of high‐resolution wind and temperature data recorded at Great Dun Fell on the high ground of the Pennines. Martin Young's article ‘Rapid temperature and wind fluctuations at a mountain site in northern England on 9/10 February 2015’ describes fascinating changes close to the level of a marked temperature inversion at this time last year. The inversion itself descended through the 847m level of this station, the change erratic and involving both rapid wind‐speed and temperature changes over very short periods. Our third paper is a preliminary review of the exceptional and record‐breaking rainfall in Cumbria in December by Stephen Burt, Mark McCarthy, Mike Kendon and Jamie Hannaford. ‘Cumbrian floods, 5/6 December 2015’ is on p. 36. On p. 40, we look at the effects on air quality of three large coal‐fired power stations in Yorkshire, as seen using satellite radiometry. In ‘Detection of the Yorkshire power stations from space: an air quality perspective’ Richard Pope and Miroslav Provod discuss the notable effects of these power stations, seen using 7‐year mean data, the level of pollution rivalling that of Manchester or London. Deciding where a high‐density network of automatic weather stations will give you the best information to plan for severe weather events is a complex matter. A great deal of information is now available for the environment, but it is necessary to bring these data together, as described in the method used in mountainous northern Turkey: ‘A GIS‐based siting technique for automatic weather stations in Trabzon, Turkey’ by Volkan Yildirim, Recep Nisanci, Ebru Husniye Colak and Okan Yildiz on p. 43.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.082 | 0.041 |
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".