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Record W2164563438 · doi:10.2980/16-3-3264

Diversity and functional groups dynamics affected by drought and fire in Patagonian grasslands

2009· article· en· W2164563438 on OpenAlexvenueno aff
Luciana Ghermandi, Sofía González

Bibliographic record

VenueEcoscience · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsForbVegetation (pathology)Species richnessEcologyFire regimeSoil seed bankSpecies diversityGrasslandEnvironmental scienceGeographyBiologyAgronomySeedlingEcosystem

Abstract

fetched live from OpenAlex

During 1998–1999 a severe drought occurred in northwestern Patagonia that provoked an extensive wildfire. We monitored vegetation cover and the soil seed bank to study the diversity and functional group gap dynamics in burned and unburned sites. Species were grouped into 3 functional groups: forbs, fugitive species, and annual grasses. Post-drought vegetation recovered quickly due to a rainy spring in the second year but decreased after a dry and warm spring in the third year. These patterns underline the close relationship that exists between phenological phases and meteorological variables. Drought decreased richness but did not affect the presence of stress-tolerant species, whereas fire increased richness by allowing the establishment of fugitive species. Species in the fugitive functional group may be fire adapted and depend on seed accumulation in the seed bank (storage effect) to coexist with other gap species. Forbs exhibited their highest vegetation cover and seed bank density in the unburned site. Global climate change suggests an increase in the frequency and amplitude of El Niño/Southern Oscillation phenomena that, in northwestern Patagonia, are related to the occurrence of drought, fire, and changes in vegetation dynamics.

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)0.000
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 score0.042
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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

Citations20
Published2009
Admission routes1
Has abstractyes

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