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Expressing stemflow commensurate with its ecohydrological importance

2018· article· en· W2891667681 on OpenAlexaff
Darryl E. Carlyle‐Moses, S. Iida, Sonja Germer, Pilar Llorens, Beate Michalzik, Kazuki Nanko, Alexander Tischer, Delphis F. Levia

Bibliographic record

VenueAdvances in Water Resources · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsThompson Rivers University
FundersJapan Society for the Promotion of ScienceMinistry of the Environment, Government of JapanForestry and Forest Products Research InstituteFriedrich-Schiller-Universität Jena
KeywordsStemflowThroughfallEcohydrologyEnvironmental scienceInterceptionPrecipitationEcosystemScale (ratio)Hydrology (agriculture)AgroforestryEnvironmental resource managementEcologySoil scienceGeographySoil waterMeteorologyGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Despite some progress, the importance of stemflow remains obscured partly due to computations emphasizing canopy interception loss. We advocate for two metrics—the stand-scale funneling ratio and the stand-scale infiltration funneling ratio—to more accurately portray stemflow inputs and increase comparability across ecosystems. These metrics yield per unit area stemflow inputs orders of magnitude greater than what would have been delivered by throughfall or precipitation alone. We recommend that future studies employ these stand-scale funnelling metrics to express stemflow commensurate with its ecohydrological importance and better conceptualize the role of stemflow in plant-soil interactions, permitting advances in critical zone science.

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)0.003
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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

Citations118
Published2018
Admission routes1
Has abstractyes

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