Bibliographic record
Abstract
Maddox, W. R. and Kwoll, E., 2024. A Review of geomorphological salt marsh research methodologies. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 727-731. Charlotte (North Carolina), ISSN 0749-0208. Increased elevation of salt marsh platforms through vertical accretion elicits significant research interest as sea level rise threatens coastlines globally. Appropriate methodologies that quantify marsh morphology will ensure that research provides comprehensive data to predict salt marsh response to increased sea level. This review encompasses historical and contemporary methodologies employed for salt marsh research and their efficacy producing data for modern examinations. Pioneering investigations in the 19th and early 20th centuries aimed to determine the origins and construction processes of salt marshes through the consideration of anecdotal evidence, in-situ observation, and survey records. Mid-century studies employed layer markers, erosion stakes, and pollen content to examine salt marsh maintenance regimes. The modern era has realized numerical models, high resolution digital data acquisition technologies, and large volume analysis software that empower researchers to reconstruct, simulate, and predict morphology with high confidence. Some older methods are unreliable, spatially limited, or obsolete as modern technologies provide more complex data; however, others are still considered to be effective in providing insights into salt marsh morphology. These foundational methods in conjunction with modelling salt marsh response to climate change impacts may be utilized to inform coastal management.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".