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Record W2016606662 · doi:10.1007/s10310-006-0232-9

Deposition pattern of precipitation and throughfall in a subtropical evergreen forest in south-central China

2006· article· en· W2016606662 on OpenAlexaff
Gong Zhang, Guangming Zeng, Guohe Huang, Yimin Jiang, Jiamei Yao, Chunyan Du, Ru Jiang, Chang Zhang

Bibliographic record

VenueJournal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsThroughfallCanopyEvergreenLeaching (pedology)PrecipitationEnvironmental scienceTree canopyStemflowDeposition (geology)ChemistryEnvironmental chemistrySoil scienceBotanySoil waterGeographyGeologyMeteorologyBiology

Abstract

fetched live from OpenAlex

The effects of dry deposition, canopy leaching, precipitation ion concentration, and precipitation H+ concentration on net throughfall flux (NTF, throughfall minus bulk precipitation) were evaluated on a seasonal basis by using a multiple regression analysis approach based on an observation period of 4 years in Shaoshan subtropical mixed evergreen forest, south-central China. Regression analysis results indicated that the estimated canopy exchange flux was the dominant factor regulating the NTF and the calculated dry deposition was a minor term. The seasonal dry deposition of base cations accounted for 15%–43% of the NTF. The NTF analysis showed that K+, Ca2+, Mg2+, Na+, and weak acids in throughfall were derived from foliar leaching and the canopy uptakes of H+, NH4+, and NO3− were from precipitation. The retention rate of proton (H+ and NH4+) in the canopy was close to the canopy leaching rate of base cations when corrected for weak acids because weak acid-induced canopy leaching did not exchange with protons, which suggested that the canopy leaching processes neutralized acid precipitation in Shaoshan forest.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

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