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Record W2047737768 · doi:10.1191/095968301674769686

1300-year tree-ring width and density series based on living, dead and subfossil black spruce at tree-line in Subarctic Quebec, Canada

2001· article· en· W2047737768 on OpenAlexafffundabout
Lily Wang, Serge Payette, Yves Bégin

Bibliographic record

VenueThe Holocene · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité LavalCenter for Northern Studies
FundersNatural Sciences and Engineering Research Council of CanadaAgencia Nacional de Investigación y Desarrollo
KeywordsSubfossilBlack spruceMaximum densityDendrochronologyChronologySeries (stratigraphy)Subarctic climatePicea abiesPhysical geographyForestryBotanyEnvironmental scienceGeographyBiologyEcologyHoloceneTaigaPhysicsArchaeologyPaleontology

Abstract

fetched live from OpenAlex

Living, dead and subfossil trees of black spiruce (Picea mariina [Mill.] BSP were used to build a 1300-year chronology based on ring width and wood density. All density sariables (maximcum, minimum, earlywood and latewood densities) among the three types of trees were simlilar, whereas ring width was significantly Higher in liviing trees thani in deacd and subfossil trees. Correlition of the indexed series from liviing and dead trees and from dead and subfossil trees that grew during the samiie periods were higher for Maximum density (r = 0.70, 0.63) and mean latewood density (r = 0.65, 0.66) than for Minimum density (r = 0.16. 0.35 and ring with (r=0.15, 0.49). respctively. Maximum density and mean latewood density were significantly correlated with all temperature variables: mean annual (January-December) and growing season (May September) temperatures, sum of degree days and frost-free days. Accordingly, Maximum and latewood density in tree-rings of spruce stem at tree-linec can be conisidered as a function Of summner-temnperature distributions and different types of trees can be combined for the reconstruction of long-term climatic trends due to theii synchronous variations.

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 score0.305
Threshold uncertainty score0.638

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 head0.013
GPT teacher head0.204
Teacher spread0.191 · 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

Citations38
Published2001
Admission routes3
Has abstractyes

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