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Record W4251658809 · doi:10.1139/x00-008

Growth response of spruce saplings in relation to climatic conditions along a gradient of gap size

2000· article· en· W4251658809 on OpenAlexvenueno aff
Igor Drobyshev, Bengt Nihlgård

Bibliographic record

VenueCanadian Journal of Forest Research · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsCanopyPicea abiesBlack spruceTaigaEnvironmental scienceBorealBiologyAtmospheric sciencesEcologyPhysical geographyGeographyGeology

Abstract

fetched live from OpenAlex

To evaluate the relative importance of climatic factors and the level of natural canopy disturbance on sapling growth rates, terminal shoot increment of Norway spruce (Picea abies (L.) Karst.) saplings was analyzed in old-growth Sphagnum-Myrtillus forests of the European southern boreal zone (Tver Region, Russia). For a 5-year period, terminal increments were retrospectively measured in 225 saplings in a range of naturally created canopy gaps. Climatic variability was estimated by Seljaninov hydrothermal coefficient. Variation in the growth rate was partitioned as (i) annual variation observed within a particular sapling over a 5-year period ("within-stem variation," WSV) and (ii) variation of 5-year cumulative height increments within a particular location ("within-location variation," WLV). Sapling growth was positively related to gap size and, except when under canopy location, with the height of the saplings. For the growth, differences in sapling location along a gradient of gap sizes were more important than annual dynamics of water availability. Impact of the annual climatic variability was less pronounced in large gap, compared with other locations. Absolute values of WSV and WLV were similar under an intact canopy. WSV had a tendency to decrease in greater gaps, whereas WLV variation increased.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.996
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.045
GPT teacher head0.305
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2000
Admission routes1
Has abstractyes

Explore more

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