Chemical de-staining and the delta correction for blue intensity measurements of stained lake subfossil trees
Bibliographic record
Abstract
Abstract. The stain of wood samples from lake subfossil trees (LSTs) is challenging the wide application of the blue intensity (BI) technique for millennial dendroclimatic reconstructions. In this study, we used seven chemical de-staining reagents to treat samples of subfossil black spruce (Picea mariana (Mill.) B.S.P.)) trees from two lakes in the eastern Canadian boreal forest. We subsequently compared latewood BI (LBI) and delta BI (DBI) time series along with conventional maximum latewood density (MXD) measured from the stained and de-stained samples. Results show that the stain of our samples is most likely caused by post-sampling oxidation of dissolved ferrous iron in lake sediments that penetrated into wood. Three reagents (ascorbic acid, sodium ascorbate and sodium dithionite all mixed with ethylenediaminetetraacetic acid) could remove > 90 % of Fe. However, even for the best chemical protocol, a discrepancy of about +2 °C compared to MXD data remains in the LBI-based temperature reconstruction due to incomplete de-staining. On the contrary, the simple mathematical delta correction, DBI is unaffected by Fe stain and shows very similar results compared to MXD data (r > 0.82) from annual to centennial timescales over the past ~ 360 years. This study underlines the difficulty of completely de-staining lake subfossil samples, while confirming the robustness of the DBI approach. DBI data measured from stained LSTs can be used to perform robust millennial temperature 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".