ORIGINAL PAPER Peat, Water and Plant Tissue Chemistry Monitoring: A Seven-Year Case-Study in a Restored Peatland
Bibliographic record
Abstract
Abstract Following ecosystem-scale restoration of a post-harvested bog in Eastern Canada, a long term monitoring program was set up. Peat, water and plant chemistry data were collected during 7 years in a restored peatland, an adjoining post-harvested non-restored peatland, and in a neighboring natural peatland to determine the potential of chemistry data for evaluating of restoration success over time. We used a multivariate Principal Response Curves (PRC) approach to study the temporal evolution of peat, water, and plant tissue chemistry. Nitrogen, iron, and sodium were the most responsive elements to restoration in the peat. Water chemistry was still different between natural and restored sections 7 years after restoration, but the difference was not constant over the growing season. The analyses of plant tissue chemistry provided useful information related to fundamental processes (nutrient retention/leaching, plant nutrition). Among other, it high-lighted P deficiencies in the non-restored section, and nutrient replenishment in the restored plant tissues. P levels appear to be a good indicator of restoration success and should be used routinely in monitoring studies. Since the three plants species tested in this study displayed different responses to restoration, we also recommend targeting key species or functional groups for monitoring.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".