Floodplain-age distribution interrogated via photogrammetry and dendrochronology: A case study from the Tsǟlnjik Chú (Nordenskiold River) of Yukon, Canada
Bibliographic record
Abstract
Meandering rivers are common features of boreal lowlands in northern Canada. As meanders grow and evolve over time, they host maturing soils and characteristic vegetation successions. Radiochronology and numerical modelling have been employed to model age distributions along individual meander surfaces, whereas dendrology has thus far remained a less-tested approach, despite its demonstrated potential as a chronologic tool for landscapes. Here we test an integration of age estimations through dendrochronology on black spruce ( Picea mariana ) and timelapse photogrammetry to determine the age structure of meandering-river floodplains, using the boreal Tsǟlnjik Chú (Nordenskiold River) in the traditional territory of the Little Salmon/Carmacks First Nation (southern Yukon) as a case study. We first find that the relationship between black-spruce size and age is best described by a positive power law, and that the apparent rates of meander migration decay exponentially with respective integration timescales ranging from 1 to 10 3 years. Dendrochronologic ages lower than ∼100 years scale linearly with a timescale metric from timelapse photogrammetry, although showing that photogrammetric timescales underestimate the true age by a factor of ∼2. The 100-yr threshold corresponds, notably, with the mean fire return interval measured in nearby black-spruce-dominated forests, although it might also be influenced by permafrost processes. These findings demonstrated the potential of dendrochronology in understanding the age structure of meandering-river floodplains, providing an alternative methodology to cross-test results from radiochronology and numerical modelling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".