MétaCan
Menu
Back to cohort
Record W2037046128 · doi:10.1038/srep05448

Scaling of nitrogen and phosphorus across plant organs in shrubland biomes across Northern China

2014· article· en· W2037046128 on OpenAlexfundno aff
Xian Yang, Zhiyao Tang, Chengjun Ji, Hongyan Liu, Wenhong Ma, Anwar Mohhamot, Zhaoyong Shi, Wei Sun, Tao Wang, Xiangping Wang, Xian Wu, Shunli Yu, Ming Yue, Chengyang Zheng

Bibliographic record

VenueScientific Reports · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersInstitute of Botany, Chinese Academy of SciencesChinese Academy of SciencesUniversity of British ColumbiaNational Natural Science Foundation of China
KeywordsShrublandNutrientAllometryShrubBiologyBiomePhosphorusEcologyBotanyAgronomyNitrogenAridEcosystemChemistry

Abstract

fetched live from OpenAlex

Allocation of limiting resources, such as nutrients, is an important adaptation strategy for plants. Plants may allocate different nutrients within a specific organ or the same nutrient among different organs. In this study, we investigated the allocation strategies of nitrogen (N) and phosphorus (P) in leaves, stems and roots of 126 shrub species from 172 shrubland communities in Northern China using scaling analyses. Results showed that N and P have different scaling relationships among plant organs. The scaling relationships of N concentration across different plant organs tended to be allometric between leaves and non-leaf organs, and isometric between non-leaf organs. Whilst the scaling relationships of P concentration tended to be allometric between roots and non-root organs, and isometric between non-root organs. In arid environments, plant tend to have higher nutrient concentration in leaves at given root or stem nutrient concentration. Evolutionary history affected the scaling relationships of N concentration slightly, but not affected those of P concentration. Despite fairly consistent nutrients allocation strategies existed in independently evolving lineages, evolutionary history and environments still led to variations on these strategies.

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)0.002
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 score0.008
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.230
Teacher spread0.225 · 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

Citations111
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicEcology and Vegetation Dynamics StudiesFrench-language works237,207