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Record W2909765868 · doi:10.1080/10549811.2018.1554496

Chemical content and seasonal variation of throughfall and litterflow under individual trees in the Hyrcanian forests of Iran

2019· article· en· W2909765868 on OpenAlexaff
Hashem Habashi, Maryam Moslehi, elham shabani, Tom Pypker, Ramin Rahmani

Bibliographic record

VenueJournal of Sustainable Forestry · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsThompson Rivers University
FundersMinisterstvo Školství, Mládeže a Tělovýchovy
KeywordsThroughfallCarpinus betulusHornbeamNutrientMapleFagaceaePotassiumBotanyQuercus roburChemistryHorticultureBiologyCanopyEcologyFagus sylvatica

Abstract

fetched live from OpenAlex

We assessed the effect of seasons and the tree species velvet maple (Acer velutinum Boiss), Hainbuche hornbeam (Carpinus betulus L.) and chestnut-leaved oak (Quercus castanefolia C. A. Mey.; Fagaceae) on nutrient fluxes (Na+, K+ and Ca2+) in throughfall (TF) and litter flow (LF) from January 2012 to February 2013. Potassium (K+) and calcium (Ca2+) in total TF and LF over the measurement period significantly differed between 3 the species (p < 0.05). Maple and oak trees had significantly higher fluxes of K+ and Ca2+ in their TF, respectively. The average quantity of nutrients in LF was significantly higher in the leafy season than in the leafless season for all cations except for Na+. Under velvet maple trees, the average flux of nutrients (Na+, K+, and Ca2+) in TF was significantly higher than LF. Lastly, leaching of potassium (K+) and calcium (Ca2+) in LF during the measurement period was significantly lower than TF under the hornbeam trees. Our results suggest that different tree species may impact the nutrient availability and possibly the sustainability of Hyrcanian forests. Further research is needed to determine how managing different species will affect the long-term nutrient status of these forests.

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 score0.004
Threshold uncertainty score0.173

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 head0.203
Teacher spread0.191 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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