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Record W2151488133 · doi:10.1002/hyp.7105

Variation in runoff with age of Chinese fir plantations in Central South China

2008· article· en· W2151488133 on OpenAlexaff
Dalun Tian, Wende Yan, Xiaoyong Chen, Xiangwen Deng, Yuanying Peng, Wenxing Kang, Changhui Peng

Bibliographic record

VenueHydrological Processes · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversité du Québec à MontréalUniversity of Toronto
Fundersnot available
KeywordsSurface runoffWatershedHydrology (agriculture)ChinaEnvironmental scienceForestryPloughRunoff curve numberPhysical geographyGeographyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Abstract To determine the influence of forest structures on runoff characteristics, the hydrological effects of Chinese fir plantations were studied by analysing runoff patterns at different growth and development stages (stand age classes I to V) from 1984 to 2004 at the Huitong Ecosystem Research Station, Central South University of Forestry and Technology, Hunan Province, Central South China. Results for two small experimental Chinese fir watersheds showed different peak values for surface runoff amount and coefficients at different ages, with lowest values in age classes I and V and highest values in age classes II and III. However, both underground and total runoff coefficients decreased with increasing age class. Total runoff coefficient was about twice as high in age class I (30·8%) as that in age class V (15·8%). Higher underground and total runoff coefficients were found in young forests. This was mainly attributed to soil disturbance due to human management practices such as site ploughing. Results indicate that Chinese fir plantations play a significant role in regulating water distribution in the watershed. Useful information is provided on the effects of forest management practices on hydrological processes in forest plantations. Copyright © 2008 John Wiley & Sons, Ltd.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.282

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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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
Published2008
Admission routes1
Has abstractyes

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