Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,017 | 0,008 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,008 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,183 | 0,114 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».