Flood rings, earlywood vessels and hydrological signal in <i>Fraxinus pennsylvanica</i> trees growing along the central Assiniboine river floodplain, southcentral Canada
Bibliographic record
Abstract
In southcentral Canada, paleoflood reconstructions have mainly focussed on identifying flood rings in upper terrace bur oak (Quercus macrocarpa) trees from the Red and Assiniboine rivers. In contrast to upper terrace trees, floodplain tree species such as green ash (Fraxinus pennsylvanica) were said a few decades ago to provide ‘noisy’ flood proxies due to a greater flood exposition. This unverified assumption about floodplain trees was tested using green ash trees growing along the floodplain of the Assiniboine river. In each sample and for each year, flood rings were visually identified, vessels with a cross-sectional area >1000 µm2 as well as earlywood, latewood and total ring width were measured. Results indicated that flood rings were replicable and that they correspond to tree rings characterized by a high number of earlywood vessels having a reduced mean area (less porous earlywood) and, in which, radial multiples were abundant. Years in which flood rings were most abundant corresponded to documented high-magnitude floods. Flood-ring, earlywood vessel (except density and total area) and ring-width chronologies were all significantly correlated to spring mean discharge. Floodplain green ash trees do capture hydrological events including extreme floods and should thus not be ignored in paleoflood studies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".