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Record W2521764939 · doi:10.1002/wat2.1187

The role of stable isotopes in understanding rainfall interception processes: a review

2016· review· en· W2521764939 on OpenAlexaff
Scott T. Allen, Richard F. Keim, Holly Barnard, Jeffrey J. McDonnell, J. Renée Brooks

Bibliographic record

VenueWiley Interdisciplinary Reviews Water · 2016
Typereview
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
Fundersnot available
KeywordsThroughfallCanopyInterceptionEnvironmental scienceStemflowStable isotope ratioHydrology (agriculture)Isotope fractionationAtmospheric sciencesSoil waterEcologySoil scienceGeology

Abstract

fetched live from OpenAlex

The isotopic composition of water transmitted by the canopy as throughfall or stemflow reflects a suite of processes modifying rainfall. Factors that affect isotopic composition of canopy water include fractionation, exchange between liquid and vapor, and selective transmittance of temporally varying rainfall along varying canopy flowpaths. Despite frequent attribution of canopy effects on isotopic composition of throughfall to evaporative fractionation, data suggest exchange and selection are more likely the dominant factors. Temporal variability in canopy effects is generally consistent with either exchange or selection, but spatial variability is generally more consistent with selection. However, most investigations to date have not collected data sufficient to unambiguously identify controlling processes. Using isotopic data for improved understanding of physical processes and water routing in the canopy requires recognizing how these factors and processes lead to patterns of isotopic variability, and then applying this understanding towards focused data collection and analysis.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.034
GPT teacher head0.302
Teacher spread0.267 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations114
Published2016
Admission routes1
Has abstractyes

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