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Enregistrement W41134075

Laboratory of Tree-Ring Research and School of Natural Resources and the Environment

2011· article· en· W41134075 sur OpenAlexaboutno aff
Thomas W. Swetnam, Donald A. Falk, Elaine Kennedy Sutherland, Peter M. Brown, Timothy J. Brown

Notice bibliographique

RevueLincoln (University of Nebraska) · 2011
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFire effects on ecosystems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTree (set theory)Natural resourceNatural (archaeology)GeographyForestryEcologyMathematicsBiologyArchaeology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Understanding the role of climate variation in governing fire regimes remains one of the central needs in contemporary fire science and management. Ideally, this understanding should encompass both historical and current fire-climatology, and inform both basic science and ecosystem management. In this project, Fire and Climate Synthesis (FACS) we undertook a detailed synthesis of both paleofire and modern fire based on compilations of existing data sets. We also analyzed three major thematic pathways by which climate has impacted fire policy, including direct and indirect climate effects on fire policy. Paleofire. We assembled the largest and most comprehensive data set of cross-dated, georeferenced fire-scar paleofire records ever compiled for western North America. Data were provided by over 60 researchers in the field, in the form of data files, published reports, and individual study records. We also accessed the most recent holdings of the International Multiproxy Paleofire Database (IMPD) for inclusion in our compilation. These efforts resulted in the compilation of 1,248 fire history studies from 64 contributors meeting our quality criteria. Study locations extend from southern Canada to north-central Mexico, and cover the 3,248-yr period 1248 BCE to 2011 CE, with sample size > 600 sites covering the period 1700-1990. Seven major forest types are represented, including piñon-juniper, pine-oak woodland, ponderosa pine woodland, dry and mesic mixed conifer, fir, and subalpine forests. Mean annual precipitation ranges from < 30 cm to > 200 cm, while mean annual temperature ranges from 5.0 to 25.3 °C. We identified numerous west-wide fire years in the paleofire record indicating the strong top-down influence of synoptic climate conditions and regulation by major climate oscillatory modes. Our work included the development of new software tools to facilitate future analyses of paleofire data sets (see Decision Support Tools, below). Modern fire. For our synthesis of modern fire-climatology, we focused on analyzing trends and drivers in area burned in western forests, particularly the influence of snowpack duration and climate variables to annual area burned. We compiled annual area burned (AAB) for the western US based on data provide by Dr. AH Westerling, University of California – Merced, for the period 1972-2006. 12,596 fires met data quality standards and were included in analysis. Similar data were obtained from the Canadian Large Fire Database. Spatiotemporal climate layers (monthly mean, minimum, and maximum temperature and precipitation) were obtained from the National Climate Data Center for the same time period. We obtained snowpack data estimating the presence or absence of snowpack from satellite reflectance data aggregated to 25 km2 pixels. Snowpack data were converted to a continuous variable, LDPS (Last Date of Permanent Snowpack). We evaluated all time series for trend over the period of analysis at the scale of each 1° grid cell. For AAB we analyzed the period 1972-1999, and climate variables and snowpack for the period 1972-2006. To separate the influence of multiple drivers, we employed path analysis to identify the relative contributions of seasonalized temperature, precipitation, and snowpack duration to AAB. AAB increased significantly over most of the study area during the period 1972-1999. Seasonalized temperatures increased, and winter precipitation and snow cover duration decreased, over most of the study area significantly over the period of analysis (1972-2006). Winter temperature and precipitation had the strongest effect on snowpack duration. In turn, snowpack duration affected AAB, but results were spatially heterogeneous. Overall, the strongest effects on AAB were the direct effects of winter and temperature, followed by direct effects of spring precipitation. Most indirect effects, i.e. mediated by climate effects on snowpack, were a relatively small component of variability. Effects of snowpack on AAB were spatially variable in strength and sign. These results suggest that snowpack duration may be an indicator of the factors that control AAB, rather than a mechanism of control. We also compiled the first complete set of “pyroclimographs” for the western US, visualizations that integrate monthly mean temperature, precipitation, and area burned from both lightning- and human-caused fires.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,126
Score d'incertitude au seuil0,420

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0030,003
Science ouverte0,0020,002
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,1260,043

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,191
Écart entre enseignants0,178 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2011
Routes d'admission1
Résumé présentoui

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