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Record W1975001555 · doi:10.1080/00103620801925836

Sequential Extractions of Elements in Tree Rings of Balsam Fir and White Spruce

2008· article· en· W1975001555 on OpenAlexaff
Daniel Houle, Marc Richer Laflèche, Louis Duchesne

Bibliographic record

VenueCommunications in Soil Science and Plant Analysis · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsEnvironment and Climate Change CanadaUniversité du QuébecMinistry of Natural Resources and Forestry
Fundersnot available
KeywordsChemistryBalsamBariumMagnesiumXylemPotassiumNitric acidManganesePhosphorusZincBotanyEnvironmental chemistryInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Many attempts have been made to reconstruct past soil chemistry from tree rings' total element concentrations. However, a few recent studies have shown that some elements are highly mobile within some tree species' sapwood, which may complicate the interpretation of temporal trends. To investigate element mobility in xylem of balsam fir (BF) and white spruce (WS), a dendrogeochemical method was utilized, which consists in sequentially extracting tree ring samples with water and diluted hydrochloric acid (HCl; 0.05 M) followed by a complete digestion in nitric acid (residual). The results show that, within the sapwood of BF and WS, potassium (K) and phosphorus (P) are found mostly in the water extract whereas divalent cations [calcium (Ca), magnesium (Mg), strontium (Sr), manganese (Mn), zinc (Zn), and barium (Ba)] are mainly present as soluble or exchangeable forms. Total xylem concentration generally decreased in the following order for both tree species: Ca > Mg > Mn > Zn, Ba > Sr. At the opposite, the vast majority of aluminum (Al) (>99%) and iron (Fe) (>95%) is found in the residual fraction, suggesting that these elements are not affected by radial reequilibration during circulation of the sap. Because Al soil availability is known to increase with decreasing pH, this element can potentially be used for past reconstruction of soil pH.

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)0.001
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 score0.136
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.053
GPT teacher head0.293
Teacher spread0.239 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

Explore more

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