Metamodelling in sustainable environmental management
Notice bibliographique
Résumé
To evaluate decision's sustainability, it is necessary to determine and assess the values of current and future welfare outcomes which, in turn, depend on the current and predicted status of the environment.These tasks make the application of models and mathematical tools unavoidable and justify the necessity of quantitative indicators of sustainability in decision and policy making, since environmental models are aimed to produce the results which complement observations on environmental parameters where they cannot be obtained directly.At the same time, concerns raised by the scientists and practitioners in recent years led to a suggestion that the complexity of the environmental models is one of the main obstacles in their wider use by the stakeholders.Therefore, complexity reduction is an important task for the successful application of the environmental models in the practical environmental decision-making and management.The term 'complexity' is used in conjunction with a computational algorithm in order to describe its efficiency during the runtime.The comparison of the complexity of any two models describing the same ecosystem must take into account the following model features: the total number of state variables included into the model, the total number of model parameters and the non-linear features of the model.It is worth noting that, in general, these three features are independent.While first two characteristics can be expressed through the complexity index, the third one cannot be easily quantified and added to the index.An obvious suggestion is that the implementation algorithms used to obtain the model solutions must also be considered in deciding on the comparative complexity of the models.In this case, the effect of non-linear terms could be taken into account, at least to some extent.Commonly used statistical approaches to building an emulator of a complex model include response surface method (RSM), neural network (NN) and kriging.In all three cases, the emulators are constructed using mathematical techniques which significantly differ from those used in the original models.This means that the emulators have sets of own parameters which do not overlap with the original model parameter sets in terms of their practical meaning or their values.Environmental models can be used in environmental management within the following settings: (1) to test possible scenarios via "what-if" analysis; (2) to find an optimal or at least satisficing scenario via optimization methods; (3) to determine key factors for a case study at hand.The replacement of an original model by an emulator looks very attractive with one reservation: it is necessary to ensure that the replacement is valid.It is obvious that in general case equal or very close values of two functions in certain points do not guarantee that their derivatives will also have close values.This means that emulators of complex environmental models can be used in the tasks which require only values of model state variables to complete the investigation.If the problem calls for optimization methods, it is necessary to ensure that the emulator contains all relevant state variables permitting to find a solution, and only non-gradient methods can be recommended to find a solutions to avoid misleading results.
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 | 0,008 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,005 | 0,008 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».