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Record W2775540316 · doi:10.14430/arctic4685

Episodic and Systematic Tree Ring-Width Variation (AD 1763–2013) in the Takhini Valley, Southwest Yukon, Canada

2017· article· en· W2775540316 on OpenAlexaffvenueabout
Wayne L Strong

Bibliographic record

VenueARCTIC · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsYukon University
Fundersnot available
KeywordsChronologyDendrochronologyClimatologyPrecipitationMoisturePhysical geographyEnvironmental scienceGeographyAtmospheric sciencesGeologyMeteorologyArchaeology

Abstract

fetched live from OpenAlex

A tree-ring analysis of 764 western white spruce (Picea albertiana) in the Takhini Valley of southwest Yukon was conducted to assess short- and long-term variation in growth and local climate. The resulting chronology spanned the period from AD 1763 to 2013. A polynomial regression (R = 0.720, p < 0.001) indicated that the pre-1840 segment of the chronology had below-normal tree ring-width index (RWI) values (average 0.64, with modest variation), but the subsequent segment had greater variation and a steady increase in RWI values (average 0.89) until ~1920. After 1930, RWI values began to increase again (average 1.06) with 51% more variation than had previously occurred. Peak RWI values after 1930 were double those of the early 1800s. RWI values were uncorrelated with air temperature variables (except September minima), but weakly and positively correlated (r < 0.35) with precipitation variables. RWI values were moderately correlated with annual heat-moisture index values (r = −0.415, p < 0.001), although more strongly with RWI values less than 1.1 (R = −0.631, p < 0.001). Therefore, the RWI chronology was interpreted from an ecological moisture-balance perspective, with possible long-term temperature changes estimated from archival sources. The latter suggested a 2.1˚ – 3.1˚C rise since the early 1800s. Extreme RWI values and portions of the chronology were associated with known environmental events.

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)0.001
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 score0.117
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.217
Teacher spread0.200 · 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

Citations5
Published2017
Admission routes3
Has abstractyes

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