Wetland sediment soil organic carbon stock and sequestration rates in undisturbed and rewetted Canadian wetlands
Bibliographic record
Abstract
"Mistry et al - Comm Earth Environ - Supp Data.XLSX" file contains supporting data and description for the manuscript "Mistry et al. Rewetting wetlands results in amplification of Natural Climate Solutions", including data on the Wetland ID, the geographical location, the year of sampling, and organic carbon (OC) data in samples collected from undisturbed and rewetted wetlands situated across Canada (Alberta, Saskatchewan, Manitoba, and Ontario), which were used to compute normality tests, descriptive statistics, frequency distribution, Spearman correlation coefficients, simple linear regression, and generalized additive model (GAM) analyses. "Mistry et al - Comm Earth Environ - R Script.R" contains an annotated script to run the simple linear regression and GAM to evaluate the influence of time since rewetting and hydro-biogeochemical factors on (1) total post-rewetting OC stock and (2) net change in OC sequestration rate post-rewetting. "Mistry et al - Comm Earth Environ - R Data.CSV" contains data designed to be used alongside the script "Mistry et al - Comm Earth Environ - R Script.R". "Mistry et al - Comm Earth Environ - R Script and Data - Readme.TXT" contains a description of "Mistry et al - Comm Earth Environ - R Script.R" and "Mistry et al - Comm Earth Environ - R Data.CSV". For details, see Mistry et al. Rewetting wetlands results in amplification of Natural Climate Solutions. Please contact Irena Creed for more information: irena.creed@utoronto.ca
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".