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Record W2030771060 · doi:10.1088/1755-1315/9/1/012016

<i>Quercus macrocarpa</i>annual, early- and latewood widths as hydroclimatic proxies, southeastern Saskatchewan, Canada

2010· article· en· W2030771060 on OpenAlexaffabout
Jessica Vanstone, D. Sauchyn

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsClimatologyGeologyEnvironmental science

Abstract

fetched live from OpenAlex

Fluctuations in size of annual ring-widths of Quercus species suggest that environmental factors influence the size and density of vessels within the ring, either by acting as a limiting factor for growth or through fine tuning of the wood structure to environmental factors. The purpose of this study is to assess the potential of Q. macrocarpa to provide multiple dendroclimatic proxies for the Canadian Prairies, by investigating growth responses of annual, early- and latewood widths to regional climate variability. Results indicate that ring width chronologies, from southeastern Saskatchewan capture regional signals related to moisture and drought conditions. Correlations suggest that late-wood widths are more representative of annual ring-widths, than are early-wood widths, and are the best proxy of seasonal fluctuations in climate. Thus regression models that include latewood widths were able to account for more variance in the Palmer Drought Severity Index (PDSI) than when annual ring-widths are used as the only proxy. This study demonstrates that Q. macrocarpa can provide multiple dendroclimatic proxies for investigating large scale climatic fluctuations at annual and sub-annual time scales. It is novel in terms of sub-annual analysis of tree-rings in a region that previously lacked dendrochronological research.

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 score0.671
Threshold uncertainty score0.961

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 studies0.0010.002
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.175
Teacher spread0.170 · 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

Citations4
Published2010
Admission routes2
Has abstractyes

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