Improved dendroclimatic calibration using blue intensity in the southern Yukon
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".