Assessing Coastal Squeeze of Tidal Wetlands
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
Torio, D.D. and Chmura, G.L., 2013. Assessing coastal squeeze of tidal wetlands.As sea level rise accelerates and land development intensifies along coastlines, tidal wetlands will become increasingly threatened by coastal squeeze. Barriers that protect inland areas from rising sea level prevent or reduce tidal flows, and impermeable surfaces prevent wetland migration to the adjacent uplands. As vegetation succumbs to submergence by rising sea levels on the seaward edge of a wetland, those wetlands prevented from inland migration will decrease in area, if not disappear completely. Tools to identify locations where coastal squeeze is likely to occur are needed for coastal management. We have developed a “Coastal Squeeze Index” that can be used to assess the potential of coastal squeeze along the borders of a single wetland and to rank the threats faced by multiple wetlands. The index is based on surrounding topography and impervious surfaces derived from light detection and ranging and advanced spaceborne thermal emission and reflection radiometry imagery, respectively, and uses a fuzzy logic approach. We assume that coastal squeeze varies continuously over the coastal landscape and tested several fuzzy logic functions before assigning a continuous weight, from 0 to 1, corresponding to the influence of slope and impervious surfaces on coastal squeeze. We then combined the ranked variables to produce a map of coastal squeeze as a continuous index. Using this index, we compare the present and future threat of coastal squeeze to marshes in Wells and Portland, Maine, in the United States and Kouchibouguac National Park in New Brunswick, Canada.
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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 | 0.003 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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