Salt marsh connectivity and freshwater versus saltwater inflow: multiple methods including tidal gauges, water isotopes, and LIDAR elevation models<sup>1</sup>This article is derived from a special session entitled “A New Hydrology: Inflow Effects on Ecosystem Form and Functioning” that took place at the February 2011 ASLO Aquatic Sciences conference in San Juan, Puerto Rico.
Bibliographic record
Abstract
The hydrologic connectivity between different components of a coastal marsh is a key wetland structural element and is essential in determining habitat value. Salt marshes are often thought of as being hydrologically connected via tidal creeks, though water bodies within a salt marsh may also be semipermanently disconnected. At a salt marsh in Matagorda, Texas, USA, there are many “isolated” ponds, located at varying elevations. Our objective was to quantify the hydrologic connectivity of spatially isolated ponds at this site. We sampled water for salinity and stable isotopes (δ 2 H, δ 18 O) to determine the relative contribution of tidal water and rainfall within each pond. We also quantified the water level at which each pond floods its banks and connects to other ponds, using a light detection and ranging (LIDAR)-based elevation model. We found that pond connectivity was driven by multiple factors, of which wind and rainfall were the most important. Salinity and isotopic values between any two sampled ponds were correlated with the water level elevation at which the ponds connected. We conclude that the connectivity of the network, and the similarity of water samples within it, profoundly changes when specific water level thresholds are exceeded by both fresh water and salt water.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".