Effects of shallow flooding on vegetation and carbon pools in boreal peatlands
Bibliographic record
Abstract
Abstract Question:What are the effects of shallow flooding on boreal peatlands on vegetation composition and size of carbon pools in the living and dead vegetation? Location:Lake 979, Experimental Lakes Area, northwestern Ontario, Canada. Methods:A boreal basin peatland complex with treed bog, open bog, and open water was experimentally flooded by raising water level ca. 1.3 m. Vegetation and above‐ground biomass were compared between pre‐flood conditions and those nine years after flooding. Peat accumulation since flooding was also quantified. Results:Flooding caused almost all trees to die, leading to a net loss of 86% of the above‐ground living plant biomass after nine years of the flooding. Floating up of peat was rapid in the central part of the basin, and the floating peat mats were characterized by newly established open bog community. Wetland types were diversified from bog into open bog, fen, and marsh, accompanied with great species turnover. Floating open bog community accumulated the greatest amount of peat since flooding. Conclusions:This study shows that shallow flooding of bog vegetation can lead to quick re‐establishment of open bog vegetation upon the floating up of peat mats as well as changes to more diverse vegetation over decadal time spans. We estimate that the carbon pools in 2002 in living and dead plant biomass since 1992 are comparable to what they were in the above‐ground biomass in 1992. Flooding caused an initial net decrease in carbon stores, but carbon in the pre‐flood living plant biomass was replaced by both carbon in dead biomass of the pre‐flood vegetation and newly sequestered carbon in new peat growth and post‐flood living plant biomass. Possible vegetation change toward bog‐dominated system could lead to increasing rate of new peat growth, which could affect future carbon sink/source strength of the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".