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Record W2964128310 · doi:10.1177/0959683619862037

Improved dendroclimatic calibration using blue intensity in the southern Yukon

2019· article· en· W2964128310 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Holocene · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsBrock UniversityWestern University
FundersNational Science Foundation
KeywordsDendroclimatologyExtant taxonProxy (statistics)ClimatologyDendrochronologyCalibrationEnvironmental sciencePhysical geographyGeologyAtmospheric sciencesGeographyArchaeologyBiology

Abstract

fetched live from OpenAlex

In north-western North America, the so-called divergence problem (DP) is expressed in tree ring width (RW) as an unstable temperature signal in recent decades. Maximum latewood density (MXD), from the same region, shows minimal evidence of DP. While MXD is a superior proxy for summer temperatures, there are very few long MXD records from North America. Latewood blue intensity (LWB) measures similar wood properties as MXD, expresses a similar climate response, is much cheaper to generate and thereby could provide the means to profoundly expand the extant network of temperature sensitive tree-ring (TR) chronologies in North America. In this study, LWB is measured from 17 white spruce sites ( Picea glauca) in south-western Yukon to test whether LWB is immune to the temporal calibration instabilities observed in RW. A number of detrending methodologies are examined. The strongest calibration results for both RW and LWB are consistently returned using age-dependent spline (ADS) detrending within the signal-free (SF) framework. RW data calibrate best with June–July maximum temperatures (Tmax), explaining up to 28% variance, but all models fail validation and residual analysis. In comparison, LWB calibrates strongly (explaining 43–51% of May–August Tmax) and validates well. The reconstruction extends to 1337 CE, but uncertainties increase substantially before the early 17th century because of low replication. RW-, MXD- and LWB-based summer temperature reconstructions from the Gulf of Alaska, the Wrangell Mountains and Northern Alaska display good agreement at multi-decadal and higher frequencies, but the Yukon LWB reconstruction appears potentially limited in its expression of centennial-scale variation. While LWB improves dendroclimatic calibration, future work must focus on suitably preserved sub-fossil material to increase replication prior to 1650 CE.

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.

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 score0.334
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.234
Teacher spread0.208 · 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