Comparison between Blue Intensity (BI) and Maximum Latewood Density (MXD) tree-ring chronologies from the North American Boreal forests
Bibliographic record
Abstract
Tree ring-width (TRW) and Maximum Latewood Density (MXD) series have been largely used to develop high-resolution temperature reconstructions for the Northern Hemisphere. The divergence phenomenon, a weakening of the positive relationship between TRW and summer temperatures, has been observed particularly in northwestern North America chronologies. In contrast, MXD datasets have shown a more stable relationship with summer temperatures, but it is costly and labor-intensive to produce. Recently, methodological advances in image analyses have led to development of a less expensive and labor-intensive MXD proxy known as Blue Intensity (BI). Here, we compare 6 newly developed BI tree-ring chronologies of white spruce (Picea glauca [Moench] Voss) from high-latitude boreal forests in North America (Alaska in USA; Yukon and the Northwestern Territory in Canada), with MXD chronologies developed at the same sites. We assessed the quality of BI in relation to MXD based on mean correlation between trees, chronology reliability based on the Expressed Population Signal (EPS), spectral properties, and the strength and spatial extent of the temperature signal. Individual BI chronologies established significant correlations with summer temperatures showing a similar strength and spatial cover than MXD chronologies. Overall, the BI tree-ring data is emerging as a valuable proxy for generating high-resolution temperature spatial reconstructions over northwestern America.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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 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".