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Record W2012356684 · doi:10.1071/wf12140

Short-term physiological effects of smoke on grapevine leaves

2013· article· en· W2012356684 on OpenAlexaff
Tina L. Bell, Scott L. Stephens, Max A. Moritz

Bibliographic record

VenueInternational Journal of Wildland Fire · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsBell (Canada)
FundersUniversity of MelbourneUniversity of SydneyFulbright AustraliaUS-UK Fulbright CommissionBushfire Cooperative Research Centre
KeywordsSmokePrescribed burnToxicologyEnvironmental scienceBiologyGeographyEcologyMeteorology

Abstract

fetched live from OpenAlex

In recent years, bushfires and prescribed burns have caused substantial economic loss to the wine industry due to smoke taint, which makes wine unpalatable. Considerable research is being done to ameliorate smoke taint but the information available about the effect of smoke on grapevines is limited. We examined the physiological effects of short-term exposure to smoke on leaves of several varieties of grapevines. Gas exchange was measured before and after short-term exposure of leaves to smoke that was produced by combustion of two different fuels. For most varieties, short-term exposure to smoke had little effect on leaf physiology. For varieties that were affected by smoke, patterns of recovery of leaf physiology depended on fuel type. Short-term exposure to smoke had, at best, no significant effect and, at worst, only temporary effects on functioning of leaves. All varieties had recovered to pre-smoke functioning within 48 h. This study will contribute to the growing body of information relevant to fire and land management agencies and the wine industry in fire-prone areas including Australia, North and South America and Europe.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.193

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 studies0.0000.000
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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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

Citations18
Published2013
Admission routes1
Has abstractyes

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