Bibliographic record
Abstract
We begin this month's issue with the first in a series of papers re‐examining one aspect associated with the sinking of the Titanic. In ‘Titanic's mirage, part 1: The enigma of the Arctic High and a cold‐water tongue of the Labrador Current’ on p. 119, Mila Zinkova presents an excellent review of the meteorology of visibility over cold waters and the areas where mixing occurs. At the beginning of 2019, radar used operationally for weather forecasting and hydrology passed a significant anniversary in the United Kingdom. On p. 128, the short paper ‘Radar for hydrological forecasting in the UK 50 years on’ describes the development and use of this essential tool. Chris Collier was involved in much of this development and his review reveals the importance of the project and its value. On p. 130, we publish the last of the papers marking the cold weather and snow of February and March 2018, the others the content of the Special Issue of March 2019. Bill Pike and his co‐authors kept records of the snow cover and other weather factors during the event in Berkshire, Suffolk and Kent. These are reproduced in ‘Weather diaries during the easterlies of February and March 2018’. An important question that is often raised in this period of anthropogenic warming is the effect of carbon dioxide on temperature. ‘Climate sensitivity: how much warming results from increases in atmospheric carbon dioxide (CO2)?’, the latest in our Climate‐change shorts series, provides an answer to this rather complex question and one that we all need to understand. Many readers will know that tropical meteorology is frequently associated with thunderstorms, but these storms vary in their frequency by area and time of year, as revealed by Omvir Singh and Pankaj Bhardwaj in ‘Spatial and temporal variations in the frequency of thunderstorm days over India’ on p. 138. The replacement of instruments always presents a challenge – not least, thermometers, the readings from which form the longest climatic series in the world. Almost all instruments have had to be replaced as automation and electronic sensing have come to the fore. A standard form of electronic thermometer and its exposure, together able to reproduce well‐established temperature series, are discussed by Ian Strangeways in ‘The replacement of mercury thermometers in Stevenson screens’ on p. 145. A growing area of research in meteorology and climatology is the association of weather with disease. This is discussed on p. 148 by in ‘The relationship between meteorological factors and the risk of bacillary dysentery in Hunan Province, China’ by Xuewen Li and her co‐authors. As we understand how weather can affect diseases, we can put into place appropriate measures to combat them. Our final paper this month is ‘Brewster's dark patch: a neglected optical phenomenon in the landscape’ – an optical phenomenon little discussed in meteorological literature. G P Können puts this right in his eminent explanation of its formation on p. 154.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.183 | 0.114 |
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".