Blue intensity, tree-ring dimensions, earlywood vessels and flood rings in floodplain Fraxinus pennsylvanica trees, central Canada
Bibliographic record
Abstract
Flood rings have been identified in numerous floodplain ring-porous tree species of the genus Fraxinus and Quercus . They are generally characterized by an earlywood having more numerous vessels with smaller cross-sectional area compared to “normal” tree ring. In recent years, blue intensity (BI) has developed as a surrogate method to determine tree-ring density. The method, so far, has been restricted to coniferous species. No studies have assessed the utility of BI in floodplain ring-porous species and its association, among others, with earlywood vessel features and hydrological conditions. In this study, twenty green ash ( Fraxinus pennsylvanica Marsh.) trees were analysed. Flood rings were identified with both cross-sectional area of all earlywood vessels ≥ 1000 µm 2 and BI measured. Results indicated strong associations among given BI, tree-ring dimension, earlywood vessel and flood-ring chronologies. For instance, both earlywood BI (especially EW10BI) and Delta BI (DBI) showed significant inversed associations with earlywood porosity and with flood-ring abundance. These BI parameters were also strongly associated with spring discharge. In green ash, tree rings formed in years leading to flood-ring formation were characterized by increased earlywood density (higher blue intensity caused by an alteration in size and arrangement of earlywood vessels) and, to a lesser extent, by reduced latewood density. The BI features derived from the earlywood and the entire tree ring both prove to effectively complement visual identification of flood ring. Blue intensity in ring-porous species may prove useful in dendrohydrological reconstructions and also in wood sciences, i.e., when estimating tree-ring density is desired. In regards to ring-porous species, further work is warranted comparing software differences in determining BI, evaluating BI signal in other species and from various hydrological regimes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".