Expanding the Yukon tree ring Blue Intensity network to assess divergence effects and enhance climate reconstructions
Bibliographic record
Abstract
The Divergence Problem (DP) – classically defined as a breakdown in the relationship between tree growth and temperature in recent decades – poses a major challenge to developing robust tree-ring based climate reconstructions. This is particularly relevant in northwestern North America (NWNA), a climatically sensitive yet underrepresented region in dendroclimatological networks. To address this gap and the implications of the DP, we developed a network of 34 white spruce sites across southern and central Yukon, using latewood Blue Intensity (LWBI) as a high-resolution proxy for maximum summer temperatures. Sites span a broad elevational range, allowing assessment of how proximity to local treeline influences climate sensitivity. LWBI chronologies from sites within 100 m of upper treeline exhibit the strongest ( r 2 = 0.46) and most temporally stable relationship with June-August (JJA) temperatures. In contrast, lower elevation chronologies display weaker coherence and declining sensitivity in the recent period that is consistent with the DP. Ring width chronologies express poor correlations and temporal stability and are not recommended for temperature reconstructions in this region. Despite near-treeline LWBI sites presenting the best option for optimising reconstructions, there is slight evidence of a non-linear relationship between LWBI and JJA temperatures, leading to the underrepresentation of extreme warm summer temperatures. The results herein offer a strategy for minimising the DP and enhancing the reliability of temperature reconstructions in NWNA through targeted site selection. However, in a warming world, a progressive weakening in temperature limitation – even at optimal sites – will likely lead to a degradation in calibration accuracy in future reconstructions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".