Prospects for dendroanatomy in paleoclimatology – a case study on <i>Picea engelmannii</i> from the Canadian Rockies
Bibliographic record
Abstract
Abstract. The continuous development of new proxies as well as a refinement of existing tools are key to advances in paleoclimate research and improvements in the accuracy of existing climate reconstructions. Herein, we build on recent methodological progress in dendroanatomy, the analyses of wood anatomical parameters in dated tree rings, and introduce the longest (1585–2014 CE) dendroanatomical dataset currently developed for North America. We explore the potential of dendroanatomy of high-elevation Engelmann spruce (Picea engelmannii) as a proxy of past temperatures by measuring anatomical cell dimensions of 15 living trees from the Columbia Icefield area. X-ray maximum latewood density (MXD) and its blue intensity counterpart (MXBI) have previously been measured, allowing comparison between the different parameters. Our findings highlight anatomical MXD and maximum radial cell wall thickness as the two most promising wood anatomical proxy parameters for past temperatures, each explaining 46 % and 49 %, respectively, of detrended instrumental July–August maximum temperatures over the 1901–1994 period. While both parameters display comparable climatic imprinting at higher frequencies to X-ray derived MXD, the anatomical dataset distinguishes itself from its predecessors by providing the most temporally stable warm season temperature signal. Further studies, including samples from more diverse age cohorts and the adaptation of the regional curve standardization method, are needed to disentangle the ontogenetic and climatic components of long-term signals stored in the wood anatomical traits and to more comprehensively evaluate the potential contribution of this new dataset to paleoclimate research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".