Runnels Reverse Mega‑pool Expansion and Improve Marsh Resiliency in the Great Marsh, Massachusetts (USA) (Dataset)
Bibliographic record
Abstract
Dataset Description:Monitoring data set accompanying the publication, " Runnels Reverse Mega-pool Expansion and Improve Marsh Resiliency in the Great Marsh, Massachusetts (USA)" in the journal Wetlands (https://doi.org/10.1007/s13157-023-01683-6). Monitoring was conducted by the Coastal Habitat Restoration Team at Jackson Estuarine Laboratory, Unviersity of New Hampshire. Dataset is broken down into 3 components:(1) Compiled dataset of the monitoring data of the project including detailed metadata on monitoring and data analysiys. Metadata and explanaitions for input data to R code can be found in the dataset.(2) Water Level Recorder Analysis R Code - R code used to process tidal water elevations from Hoboware CSV files(3) Multivariate Analysis R Code - R code used to conduct non-metric dimensional ordination, PERMANOVA, and SIMPER analyses on the vegetation dataset Abstract:Coastal ecologists in New England have been implementing a restoration strategy of runnels, or shallow ditches, to enhance drainage of oversaturated and ponding interior marshes. In 2015, runnels were constructed to drain two large and expanding pools in the Great Marsh System of Massachusetts, USA. Vegetation, elevation, and hydrology were monitored using field sampling and remote sensing analysis conducted pre- and post-restoration over seven growing seasons to document the recovery of the vegetation community in the pool and salt marsh platform. Vegetation was monitored with 0.5 m2 plots with all species identified and percent cover estimated per species. Elevation was recorded with either laser level (2015) or RTK-GPS (2016, 2021) in the plots. Water level elevations were monitored with Odyssey capacitance loggers (2015, 2016) and Hobo pressure transducers (2018, 2021).Contact Information:Questions about the data set can be directed towards Grant McKown, james.mckown@unh.edu or jgrantmck@gmail.com
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.022 |
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".