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Record W3015393652 · doi:10.1163/22941932-bja10007

Cambial phenology in Abies pindrow (Pinaceae) along an altitudinal gradient in northwestern Himalaya

2020· article· en· W3015393652 on OpenAlexaff
Rayees A. Malik, Sergio Rossi, Raman Sukumar

Bibliographic record

VenueIAWA Journal - KU Leuven/IAWA Journal · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsPhenologyAltitude (triangle)XylemCambiumPinaceaeEcosystemGrowing seasonBiologyBotanyClimate changeHorticultureEcologyPinus <genus>

Abstract

fetched live from OpenAlex

Abstract Climate change is expected to be heterogeneous across the world, with high impacts on the Himalayan ecosystems. There is a need to precisely document cambial phenology and wood formation in these regions to better understand climate-growth relationships and how trees face a warming climate. This study describes the dynamics of cambial phenology in pindrow fir ( Abies pindrow ) along its altitudinal gradient in the Himalaya. The stages of xylem phenology, and the duration and rate of wood formation were assessed from anatomical observations during the growing season from samples collected weekly from three sites at various altitudes (2392–2965 m a.s.l.) over two years. There were significant differences in the duration and rate of cell formation along the altitudinal gradient, which decreased at increasing altitudes. The growing season duration decreased by 5.2 and 3.7 days every 100 m of increase in altitude in 2014 and 2015, respectively, while the rate of cell formation decreased from 0.38 and 0.44 cells /day to 0.29 and 0.34 cells/day in 2014 and 2015, respectively. Cell production decreased from 63.3 and 67.0 cells to 38.3 and 45.2 cells with a decrease of 4.3 and 3.8 cells per 100 m increase in altitude in 2014 and 2015, respectively. The higher precipitation in 2015 increased the growth rate and resulted in a higher xylem production. Our findings give new insights into the dynamics of cambial phenology and help in better understanding of the potential impacts of climate change on tree growth and forest productivity of Himalayan 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient 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 score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.003
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 head0.037
GPT teacher head0.256
Teacher spread0.219 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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