Complexity, Variance, and Nonlinearity: A Multisite, Multiseasonal Study of Tidal Marsh Sedimentation Processes in the St. Lawrence Estuary, Canada
Bibliographic record
Abstract
Cayer, D. and Hatvany, M., 2023. Complexity, variance, and nonlinearity: A multisite, multiseasonal study of tidal marsh sedimentation processes in the St. Lawrence Estuary, Canada. Journal of Coastal Research, 39(6), 1044–1067. Charlotte (North Carolina), ISSN 0749-0208. A review of the international literature demonstrates the high potential of intertidal marshes for sediment sequestration and growth over time. Surprisingly few studies, however, discuss the sediment pathways that occur both seasonally and annually or the magnitude of such transits. Using a multifactorial approach that accounts for asymptotic evolution, this study examines and compares four marsh sites in the maximum turbidity zone of the St. Lawrence Estuary. The aim is to determine the role of intrinsic and extrinsic factors, ecogeomorphologic feedbacks, and coastal configuration on sediment dynamics. The hypothesis is that multiagency results in nonlinear evolution trends that are site specific. The methodology comprises interannual and intersite surface-height measures, sediment concentration values, tidal inundation frequency, relative sea-level data, and a weather activity index. The results reveal variable sediment deposition rates and surface-height evolution across the marsh profile over multiple seasons and between sites. They also conform with the understanding of asymptotic evolution and illustrate a multifactorial agency driving current dynamics. As hypothesized, the evolution of the studied marshes was highly variable and nonlinear across time and space, demonstrating complex sediment pathways. Recognition of this variability brings into question the generalizability of tidal marsh dynamics in large systems. Ultimately, modelling the trajectories and predictability of future marsh evolution in large systems such as the St. Lawrence Estuary requires a multifactorial approach over multiple seasons and at multiple sites.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".