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Life span and biomass allocation of stunted black spruce clones in the subarctic environment

2000· article· en· W2058048603 on OpenAlexafffund
Marie‐Josée Laberge, Serge Payette, Jean Bousquet

Bibliographic record

VenueJournal of Ecology · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLongevityBiologyBlack spruceShrubFragmentation (computing)BotanySubarctic climateStipuleHabitBiomass (ecology)CharcoalLife spanHorticultureEcologyTaigaChemistryEvolutionary biology

Abstract

fetched live from OpenAlex

Summary 1 Slow growth, maintenance of a high leaf : wood ratio and adoption of a clonal growth habit, more than size per se , may increase the life span in trees species. The longevity of black spruce ( Picea mariana (Mill.) BSP.) is increased from 200 to 300 years, when it grows as a clonal shrub. 2 We measured the surface area and above‐ and below‐ground biomass of 25 postfire, stunted black spruce clones identified from RAPDs markers. The maximum age of each clone was deduced from tree‐ring dating or by radiocarbon dating of charcoal fragments. The oldest clone was > 1800 years of age. The total surface area of the clones increased with age, ranging from 2.8 to 691.3 m 2 . The ratio of living aerial parts to the total surface area decreased from 100% to < 50% with postfire stand age, reflecting fragmentation of clones into many autonomous or potentially autonomous rooted branches (layers). The number of layers increased with age from 12 layers in a 100‐year‐old individual to more than 80 in a 1800‐year‐old clone. 3 Biomass allocation and fragmentation can explain the maintenance of a relatively stable leaf : wood ratio of approximately 10% through time in stunted black spruce clones. The fragmentation of layers is the main mechanism ensuring the great longevity of prostrate clones in exposed sites. In the absence of perturbation, stunted black spruce clones may perpetuate for centuries or even millennia.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.448

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)0.0000.000

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 head0.017
GPT teacher head0.196
Teacher spread0.179 · 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

Citations53
Published2000
Admission routes2
Has abstractyes

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