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Record W2156227993 · doi:10.1093/treephys/28.8.1221

Ecophysiology and growth of advance red spruce and balsam fir regeneration after partial cutting in yellow birch-conifer stands

2008· article· en· W2156227993 on OpenAlexaff
Daniel Dumais, Martine Prévost

Bibliographic record

VenueTree Physiology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsMinistère des Ressources naturelles et des ForêtsMinistère des Ressources naturelles et des Forêts (Québec)
Fundersnot available
KeywordsBalsamEcophysiologyRegeneration (biology)BotanyBiologyStand developmentForestryHorticultureEcologyGeographyPhotosynthesis

Abstract

fetched live from OpenAlex

We investigated ecophysiological and growth responses of short (0.4 to 1.3 m in height) advance regeneration of red spruce (Picea rubens Sarg.) and balsam fir (Abies balsamea L.) six years after removal of 0, 40, 50, 60 and 100% of the overstory basal area (BA) in two yellow birch-conifer stands. Partial cuts significantly increased stomatal conductance of red spruce only. Light-saturated photosynthesis (leaf-area basis) of both species increased with BA removal, but unlike red spruce, specific leaf area (SLA) of balsam fir decreased with increased cutting intensity. Partial cuts appreciably increased the concentration of N and Ca in red spruce and balsam fir foliage, respectively, and resulted in decreased foliar concentrations of K in red spruce and Mg in balsam fir. The height and lateral growth of both species increased with BA removal, although partial cuts were more beneficial to balsam fir. The data suggest that short advance regeneration of red spruce and balsam fir can coexist under partial overstory conditions, but balsam fir has physiological characteristics and a capacity for morphological adjustment (SLA) that places it at an advantage when in competition with red spruce.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.398

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 studies0.0000.001
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 head0.007
GPT teacher head0.204
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.

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

Citations36
Published2008
Admission routes1
Has abstractyes

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