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Record W3046189623 · doi:10.1029/2020gl088745

Surface Wetness as an Unexpected Control on Forest Exchange of Volatile Organic Acids

2020· article· en· W3046189623 on OpenAlexfundno aff
S. Ryan Fulgham, Dylan B. Millet, Hariprasad D. Alwe, Allen H. Goldstein, Siegfried Schobesberger, Delphine K. Farmer

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersUniversity of TorontoAcademy of FinlandU.S. Forest ServiceNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsDewDew pointCanopyEnvironmental scienceVolatility (finance)Sink (geography)Tree canopyPartition coefficientAtmospheric sciencesChemistryEnvironmental chemistryBotanyGeologyCondensationMeteorologyPhysicsBiologyChromatography

Abstract

fetched live from OpenAlex

Abstract We report bidirectional exchange of volatile acids, including isocyanic and alkanoic acids, over a pine forest across multiple seasons. The exchange velocity of these acids is well correlated with dew point depression, suggesting an equilibrium‐driven continuum of flux. Wetness on forest surfaces impacts the vertical exchange of gases, and we suggest that water films and droplets drive equilibrium partitioning, with acids being solvated in surface wetness and released through evaporation. Despite their volatility, these acids partition into neutral‐to‐alkaline aqueous films, consistent with reported dew pH. This relationship between exchange velocity and dew point depression holds for a wetter mixed forest, but not a very dry orchard. Dew point depression is an excellent indicator of acid fluxes so long as the canopy is occasionally wetted.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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

Citations30
Published2020
Admission routes1
Has abstractyes

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