MétaCan
Menu
Back to cohort
Record W4365504597 · doi:10.1139/cjb-2022-0123

The woody plant <i>Haloxylon ammodendron</i> (C.A. Mey.) provides new insights into the self-thinning relationship and size inequality with wind erosion stress

2023· article· en· W4365504597 on OpenAlexvenueno aff
Yanxia Li, Enbo Liu, Kangkang Mi, Song Yu-yang

Bibliographic record

VenueBotany · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsnot available
Fundersnot available
KeywordsHaloxylon ammodendronThinningShrubBiologyCanopyCompetition (biology)Ecology

Abstract

fetched live from OpenAlex

The self-thinning process and size inequality of plants change with age and wind erosion stress conditions. In this study, we used Haloxylon ammodendron (C.A. Mey.) shrubs in their juvenile stage (1–6 years) as the study subjects. Their density, height, and survival were investigated. The relationship between the mean and variation in the densities was calculated using Taylor’s power law, and the change laws of the density clustering state were analyzed. Additionally, changes in size inequality and the self-thinning process were examined. The results indicated that under the same wind erosion stress, the self-thinning slope was positive ( P &lt; 0.05); the shrub density aggregation of H. ammodendron became denser with increasing wind erosion stress. The size inequality first increased and then decreased as the developing stage progressed. Under the same stress, the competition index increased with increasing age and decreased with increasing stress. We demonstrated that under a harsher microenvironment for plant growth, plants’ clustering and growth increased facilitation, resulting in a shift in the net effect of the positive and negative interactions between individuals. Furthermore, the results revealed that as age increased, the size inequality was left-skewed.

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 score0.017
Threshold uncertainty score0.658

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 studies0.0010.000
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 head0.015
GPT teacher head0.217
Teacher spread0.202 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBotanySame topicAeolian processes and effectsFrench-language works237,207