MétaCan
Menu
Back to cohort

Interactive effects of ozone and climate on tree growth and water use in a southern Appalachian forest in the USA

2007· article· en· W2004234428 on OpenAlexaff
S. B. McLaughlin, M. Nosal, Stan D. Wullschleger, Ge Sun

Bibliographic record

VenueNew Phytologist · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsUniversity of Calgary
FundersOak Ridge National LaboratoryNational Park ServiceU.S. Forest ServiceUT-BattelleAuburn UniversityBattelleU.S. Department of Energy
KeywordsDeciduousEnvironmental scienceOzoneEcologyClimate changeAtmospheric sciencesBiologyGeographyGeologyMeteorology

Abstract

fetched live from OpenAlex

* A lack of data on responses of mature tree growth and water use to ambient ozone (O(3)) concentrations has been a major limitation in efforts to understand and model responses of forests to current and future changes in climate. * Here, hourly to seasonal patterns of stem growth and sap flow velocity were examined in mature trees from a mixed deciduous forest in eastern Tennessee (USA) to evaluate the effects of variations in ambient O(3) exposure and climate on patterns of stem growth and water use. * Ambient O(3) caused a periodic slowdown in seasonal growth patterns that was attributable in part to amplification of diurnal patterns of water loss in tree stems. This response was mediated by statistically significant increases in O(3)-induced daily sap flow and led to seasonal losses in stem growth of 30-50% for most species in a high-O(3) year. * Decreased growth and increased water use of mature forest trees under episodically high ambient O(3) concentrations suggest that O(3) will amplify the adverse effects of increasing temperatures on forest growth and forest hydrology.

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 score0.198
Threshold uncertainty score0.394

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 head0.016
GPT teacher head0.229
Teacher spread0.212 · 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

Citations125
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueNew PhytologistSame topicPlant responses to elevated CO2French-language works237,207