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Record W2181010188 · doi:10.1139/x2012-095

Xylogenesis in black spruce subjected to rain exclusion in the field<sup>1</sup>This article is one of a selection of papers from the 7th International Conference on Disturbance Dynamics in Boreal Forests.

2012· article· en· W2181010188 on OpenAlexaffvenueabout
Evelyn Beliën, Sergio Rossi, Hubert Morin, Annie Deslauriers

Bibliographic record

VenueCanadian Journal of Forest Research · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsBlack spruceXylemPhenologyTaigaCanopyGrowing seasonBorealEnvironmental scienceTracheidDisturbance (geology)Woody plantBiologyEcologyAtmospheric sciencesAgronomyForestryBotanyGeographyGeology

Abstract

fetched live from OpenAlex

The predicted climate warming and more frequent and longer droughts are expected to produce potentially severe water stresses in the boreal forest. The aim of this experiment was to study the effect of a summer drought on xylem phenology and anatomy of mature black spruce ( Picea mariana (Mill.) BSP) trees in their natural environment. The trees were excluded from rain during June–September 2010 by the installation of under-canopy roofs in four sites of the boreal forest of Quebec. Xylem phenology, stem radius variations, and physiological traits of treated and control trees were monitored at short time resolution. At the end of the growth season, cell characteristics were measured. The rain exclusion reduced the cell area of the xylem, but no significant change was observed in cell wall thickness, cell production, or phenology. Stem radius variations of the treated trees were lower but followed the same pattern as the control. After removal of the exclusion, trees and soil quickly recovered their normal water status. One summer of drought led to the formation of smaller tracheids but showed that black spruce is resistant to this rain exclusion treatment. This is likely due to the ability to collect water from sources other than the superficial soil horizon.

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)0.001
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 score0.644
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.293
Teacher spread0.253 · 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

Citations30
Published2012
Admission routes3
Has abstractyes

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