MODELING COLD CLIMATE SALTMARSH EVOLUTION: TOOLS FOR RESTORATION AND PREDICTION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Novel approaches to evaluating marsh eco-geomorphic evolution are being developed using mathematical models that incorporate ecological, hydrological, and geomorphologic considerations. Such works have predominantly been implemented for marshes located in the Netherlands (e.g., Gourgue et al., 2022), with a couple case studies in the United States (e.g., Brand et al., 2022) and Australia (Kumbier et al., 2022). Inputs to such models are often highly site-specific and intrinsically tied to geographically variant parameters (species, sediment supply, hydrodynamic context, seasonal effects). Numerical models of marsh eco-geomorphic evolution developed thus far have not been validated for field sites within Canada. Presently, vegetation-based coastal adaptation strategies, including coastal marsh restoration design and erosion risk assessment, are hindered in Canada by a lack of numerical predictive tools that can accurately assess marsh eco-geomorphologic evolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it