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Record W2058745315 · doi:10.1071/wf06081

Relationships between seasonal patterns of live fuel moisture and meteorological drought indices for Mediterranean shrubland species

2007· article· en· W2058745315 on OpenAlexaboutno aff
Grazia Pellizzaro, Carla Cesaraccio, Pierpaolo Duce, Andrea Ventura, Pierpaolo Zara

Bibliographic record

VenueInternational Journal of Wildland Fire · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsWater contentShrublandEnvironmental scienceMediterranean climateMoistureShrubCanopyAtmospheric sciencesClimatologyGeographyMeteorologyEcologyEcosystemBiology

Abstract

fetched live from OpenAlex

Measurements of seasonal patterns of live fuel moisture content and ignitability (in terms of time to ignition) of four Mediterranean shrub species were performed in North Western Sardinia (Italy). Relationships between the two variables were evaluated. Relationships between live fuel moisture content and environmental conditions (i.e. rainfall, air temperature and soil moisture) were analysed. Two groups of species were identified in relation to the different response of live fuel moisture content to seasonal meteorological conditions. Seasonal patterns of live fuel moisture content were also compared with five meteorological drought indices: Duff Moisture Code and Drought Code of the Canadian Forest Fire Weather Index System, Keetch–Byram Drought Index, Canopy Drought Stress Index and Cumulative Water Balance Index. In addition, the capability of the meteorological drought indices to describe moisture variation for each species was evaluated. Although the Drought Code was formulated to describe changes in the moisture content of dead fuel, it was shown to have a good potential for modelling live fuel moisture variation of a group of shrubland species that are sensitive to meteorological conditions, with a clear and large decrease of moisture content during the drought season.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.258
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations138
Published2007
Admission routes1
Has abstractyes

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