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Record W2900470109 · doi:10.1139/cjb-2018-0112

Intra-annual variability in isotopic and total nitrogen in tree rings of old growth Sitka spruce from coastal British Columbia

2018· article· en· W2900470109 on OpenAlexafffundvenueabout
T. E. Reimchen, Estelle Arbellay

Bibliographic record

VenueBotany · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of Victoria
FundersHakai InstituteDavid Suzuki FoundationTula Foundation
KeywordsDendrochronologyAnnual growth %NitrogenRange (aeronautics)Physical geographyBiologyGrowing seasonEcologyBotanyEnvironmental scienceAtmospheric sciencesGeographyChemistryGeology

Abstract

fetched live from OpenAlex

Tree-ring studies using increment cores have increasingly measured elemental (N) and isotopic (δ 15 N) nitrogen values to evaluate environmental changes in the nitrogen cycle. The paucity of nitrogen in wood has constrained tree-ring analyses to annual resolution. Based on 77 rings and 310 sub-rings, we provide evidence for substantial intra-annual variability in N and δ 15 N values in the heartwood of 11 geographically widely separated, old growth Sitka spruce [Picea sitchensis (Bong.) Carrière] trees from coastal British Columbia. The range of N and δ 15 N values within rings (intra-annual level) was on average equal to or up to seven-times the range among rings (inter-annual level). Most rings showed a seasonal reduction in N values from early to late growth and a corresponding increase to the early growth of the following season (P < 0.05 to 0.001, Mann–Whitney–Wilcoxon test). By contrast, intra-annual changes in δ 15 N values were highly variable among years and among trees, ranging from pronounced oscillations (4.0‰) to reasonable consistency. Our results allude to the potential importance of such intra-ring data for interpreting seasonal trends in nitrogen use and increasing understanding of ecological processes in the marine–terrestrial interface.

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 score0.449
Threshold uncertainty score0.857

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)0.0010.000

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 head0.007
GPT teacher head0.195
Teacher spread0.189 · 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

Citations6
Published2018
Admission routes4
Has abstractyes

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