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Record W2107762586 · doi:10.1080/02827581.2012.670726

Decomposition of <i>Abies faxoniana</i> litter varies with freeze–thaw stages and altitudes in subalpine/alpine forests of southwest China

2012· article· en· W2107762586 on OpenAlexfundno aff
Jianxiao Zhu, Xinhua He, Fuzhong Wu, Wanqin Yang, Bo Tan

Bibliographic record

VenueScandinavian Journal of Forest Research · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
FundersHORIZON EUROPE Excellent ScienceProgram for New Century Excellent Talents in UniversitySouthwest UniversityNational Natural Science Foundation of ChinaSt. Francis Xavier University
KeywordsAltitude (triangle)Subalpine forestAlpine climateLitterEnvironmental scienceLigninMontane ecologyGrowing seasonAgronomyAnimal scienceHorticultureBotanyEcologyBiology

Abstract

fetched live from OpenAlex

Freeze-thaw events in winter may affect litter decomposition in cold biomes but few reports are available. We characterized the fir (Abies faxoniana) litter decomposition over a whole winter (November 2008 to April 2009) during the late autumn, deep winter, and early spring stages. The mass loss, nutrient release, and quality change of fir litter were determined using the litterbag method at 2700, 3000, 3300, and 3600 m altitude in southwest China. Over the winter an average of 18% mass, 27% C, 50% N, 40% P, 36% K, 30% cellulose, and 14% lignin were lost. Of these total losses, a majority loss of mass (70%), C (65%), N (50%), P (58%), K (42%), cellulose (70%), and lignin (68%) occurred during the deep winter stage. The highest loss rate of mass (19.2%) and lignin (16.4%) but the lowest N loss (47.9%) was at the highest 3600 m altitude. Soil freeze-thaw cycle resulted in significant losses of mass, while mass loss rate did not increase under the higher mean soil temperature during each stage. Our results confirmed that the physical process seemed to be the most important process for cold season decomposition in the cold biome.

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 score0.019
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.295
Teacher spread0.270 · 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

Citations64
Published2012
Admission routes1
Has abstractyes

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