Notice bibliographique
Résumé
he use of probabilities in weather forecasting has become the most common way of conveying the chance of occurrence of a future event. Although accepted as a standard tool, the concept of probability involves a great deal of complexity that is sometimes not appreciated in our community. For years there have been several schools of thought regarding the interpretation of probability, and the debate includes fi elds of study as diverse as philosophy, risk theory, and artifi cial intelligence. Th ere are fundamental diff erences among these interpretations, and these are not dimin-ishing with our growing scientifi c understanding.Until a few years ago, most members of the me-teorological community were satisfi ed to think of probabilities as either the product of past knowledge projected into the future (as when a histogram of fre-quencies of occurrence is viewed as a probability dis-tribution), or as a personal degree of belief regarding the occurrence of the event (based on several sources of information, but ultimately a personal opinion). Although both interpretations satisfy the conditions of probability calculus, one could wonder (and many have wondered) if these two interpretations are really describing the same thing.Th e situation has not improved with the arrival of ensemble forecasting. Now, the probability of the occurrence of an event can be obtained as a direct result of numerical forecasting, suggesting that this probability may be linked to the intrinsic predict-ability of the event. Th is may make the meaning of probability an altogether diff erent thing. And, as is the case with any forecast variable, questions arise about the error or uncertainty associated with this predicted probability. For example, ensemble forecast systems of similar skill from diff erent weather offi ces routinely disagree over the probability of occurrence of some future events. What, then, is the uncertainty of our measure of uncertainty? Could it sometimes be as large as the entire [0,1] interval, the formulated probability value being therefore meaningless?This question is often only academic for three reasons: fi rst, for most common and recurrent events, past probabilities can be verifi ed against outcomes so as to give a sense of prediction skill. Second, the waiting time between the forecast and the event is normally too short to allow for a lengthy discussion or for predictability to become very poor; and third, because what is at stake to users may not be important or contentious enough to justify such a debate.However, forecasting is now being pushed to the limits of what we know, partly by pressures exerted by society for meteorologists to deliver increasingly accurate and timely forecasts, and here the use and meaning of probability becomes problematic. We can fi nd examples of this in the forecasting of rare ex-treme events (e.g., such as the prediction, by a member of an ensemble forecast system, of a hurricane landfall in an area usually free of these kinds of storms), but a clearer case emerges in climate change studies. Th e impact of CO
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 | 0,015 | 0,074 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,042 |
| Communication savante | 0,013 | 0,031 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,006 | 0,019 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,004 |
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 ».