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Record W2258866930 · doi:10.1086/683534

Hydrology and fire regulate edge influence on microclimate in wetland forest patches

2015· article· en· W2258866930 on OpenAlexfundno aff
Adam C. Watts, Leda N. Kobziar

Bibliographic record

VenueFreshwater Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersMcGill UniversityNational Park ServiceUniversity of ChicagoNational Science Foundation
KeywordsMicroclimateEnvironmental scienceTaxodiumCypressHydrology (agriculture)WetlandEcosystemVapour Pressure DeficitVegetation (pathology)SwampEcologyAtmospheric sciencesTranspirationGeologyBiology

Abstract

fetched live from OpenAlex

Differences in temperature, relative humidity, and vapor pressure deficit (VPD) were observed within small patches of pondcypress (Taxodium distichum var. imbricarium) compared with adjacent, largely treeless, vegetation communities. These patches, locally called cypress domes, displayed increasing differences in microclimate between their exterior or edges and interiors with increasing distance toward their centers. We sought to assess whether seasonal inundation of these small swamps results in a hydrologic switch in which the presence of standing water overwhelms the effects of edge distance on microclimate variation. Hydrology does appear to exert substantial control over the edge effect on microclimate, but the effects of distance from the edge remained discernable during periods of inundation. We also studied the effects of a wildfire that occurred 2 y before data collection. We expected to find warmer and drier conditions in domes affected by recent fire. Contrary to our expectations, previously burned domes were either similar to unburned domes or, in some cases, cooler and more humid than unburned domes, possibly because of vigorous regrowth after fire in these highly productive, fire-adapted subtropical ecosystems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2015
Admission routes1
Has abstractyes

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