High-Resolution Dendrochemical Analysis of Flood-Affected Oaks using Laser Ablation Icp-Mass Spectrometry
Bibliographic record
Abstract
Dendrochemical analysis was conducted on Quercus macrocarpa Michx. (bur oak) growing within the floodplain of the Red River, Canada, to determine if xylem formed during severe floods contained significantly perturbed chemistry. Wood samples were taken from five living trees in the same stand and from three timbers from a 19th-century building. Laser ablation ICP-mass spectrometry was used to determine the trace element chemistry in the earlywood and latewood of rings formed before, during and after major floods. Between-tree correlations were significant (p < 0.05) for Hg, Mg, Mn, Sr and Zn, suggesting that changes in the abundance of these elements over time might reflect common physiological processes or environmental signals. Mg, Mn and Sr concentrations were higher in the earlywood than in the latewood for both living and historical samples. These intra-annual shifts may reflect varying rates of uptake driven by seasonal changes in anatomical development, as well as stability of these elements after their initial deposition. Although no chemical anomalies are associated with 20th-century floods, earlywood coinciding with an extreme flood in 1826 contained low levels of Mg, Mn and Sr. These elemental anomalies might represent an independent biochemical flood signature but it is also possible they are a secondary expression of wood formation disrupted by flooding. In either case, elemental analysis does not provide any information beyond that obtained from studying anatomical features, and does not appear to be a viable tool to identify the occurrence of past floods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".