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Interactive effects of ozone and climate on water use, soil moisture content and streamflow in a southern Appalachian forest in the USA

2007· article· en· W2039793470 on OpenAlexaff
S. B. McLaughlin, Stan D. Wullschleger, Ge Sun, M. Nosal

Bibliographic record

VenueNew Phytologist · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of Calgary
FundersOak Ridge National LaboratoryNational Park ServiceU.S. Forest ServiceUT-BattelleAuburn UniversityBattelleU.S. Department of Energy
KeywordsStreamflowEnvironmental scienceCanopyTranspirationWater contentCanopy conductanceHydrology (agriculture)Stomatal conductanceClimate changeTree canopyOzoneAtmospheric sciencesVapour Pressure DeficitEcologyDrainage basinGeographyBiologyGeologyMeteorology

Abstract

fetched live from OpenAlex

* Documentation of the degree and direction of effects of ozone on transpiration of canopies of mature forest trees is critically needed to model ozone effects on forest water use and growth in a warmer future climate. * Patterns of sap flow in stems and soil moisture in the rooting zones of mature trees, coupled with late-season streamflow in three forested watersheds in east Tennessee, USA, were analyzed to determine relative influences of ozone and other climatic variables on canopy physiology and streamflow patterns. * Statistically significant increases in whole-tree canopy conductance, depletion of soil moisture in the rooting zone, and reduced late-season streamflow in forested watersheds were detected in response to increasing ambient ozone levels. * Short-term changes in canopy water use and empirically modeled streamflow patterns over a 23-yr observation period suggest that current ambient ozone exposures may exacerbate the frequency and level of negative effects of drought on forest growth and stream health.

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 score0.235
Threshold uncertainty score0.467

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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

Citations107
Published2007
Admission routes1
Has abstractyes

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