Response of vegetation and carbon accumulation to changes in precipitation and water table depths in two bogs during the Holocene: a modelling exercise
Bibliographic record
Abstract
To assess the influence of hydrological changes on northern peatland ecosystems, we analysed the response of the Holocene Peat Model (HPM, Frolking et al. 2010), designed to simulate peatland development at millennial timescale, to two hydrological settings, based on precipitation and water table depths reconstructions. The studied sites are two open ombrotrophic peatlands located in the James Bay Lowlands in Northeastern Canada. For both sites, two simulations were realised: one based on a precipitation reconstruction from pollen data, used as input in the model, and a second using a water table depth reconstruction derived from testate amoebae to apply a water table forcing on the model. Simulated variations in carbon accumulation rates (CAR) and vegetation composition were analysed against the palaeoecological datasets. Results in CAR in both sites and hydrological settings showed periods of net carbon loss, which coincided with fluctuations in observed CAR, though they cannot be traced in palaeoecological datasets. The comparison between plant macrofossils records and simulated vegetation distributions highlighted differences between precipitation and water table depth driven simulations that can be used to distinguish the origin of vegetation shifts. The methodology used could thus be useful in paleoecological studies when two or more proxies are available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".