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Record W2564890402 · doi:10.1093/jpe/rtw128

Altitudinal patterns of maximum plant height on the Tibetan Plateau

2016· article· en· W2564890402 on OpenAlexafffund
Lingfeng Mao, Shengbin Chen, Jinlong Zhang, Guangsheng Zhou

Bibliographic record

VenueJournal of Plant Ecology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Alberta
FundersUniversity of British Columbia
KeywordsAltitude (triangle)Herbaceous plantEcologyPlateau (mathematics)Elevation (ballistics)Range (aeronautics)BiologyWoody plantPlant speciesPhysical geographyGeographyMathematics

Abstract

fetched live from OpenAlex

Several studies have shown that plant height changes along environmental gradients. However, altitudinal patterns of plant height across species are still unclear, especially in regions sensitive to climate change. As canopy height decreases dramatically near the tree line in alpine areas, we hypothesize that plant height across all species also decreases with increasing altitude, and distinct thresholds exist along this gradient. Using a large dataset of maximum plant height and elevation range (400 to 6000 m a.s.l.) of 4295 angiosperms from the regional flora of the Tibetan Plateau, we regressed plant height for every 100 m belt against elevation to explore the relationships. To identify the approximate boundaries where dramatic changes in plant height occurs for herbaceous plants, shrubs, trees, woody plants and all angiosperms, we used piecewise linear regression. Phylogenetically independent contrast was used to test the potential evolutionary influences on altitudinal patterns at the family level. Results showed that for herbaceous plants, shrubs, trees, woody plants and all angiosperms, plant height decreases significantly as altitude increases. In addition, we found that altitude, a proxy for many environmental factors, had obvious thresholds (breakpoints) dictating patterns of plant height. The results of phylogenetically independent contrast also emphasized the importance of evolutionary history in determining the altitudinal patterns of plant height for some growth forms. Our results highlight the relative intense filtering effect of environmental factors in shaping patterns of functional traits and how this could vary for different ranges of environmental variables.

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 categoriesInsufficient payload (model declined to judge)
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 score0.999

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)0.0020.000

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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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.

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

Citations26
Published2016
Admission routes2
Has abstractyes

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