Evaluation of the Holocene Peat Model with Data from Boreal and Subarctic Peatlands of the James Bay Lowlands, Quebec, Canada
Bibliographic record
Abstract
The Holocene Peat Model (HPM) is a dynamic model simulating the transient evolution of a peatland since its early stages. HPM takes into account the feedbacks between vegetation, peat properties, water table depth, and climate. The aim of this study is to evaluate the HPM by means of empirical data. Three distinct sampling sites were chosen within a large region including boreal and subarctic peatlands in the James Bay lowlands, northern Quebec, Canada. One fen and one bog were selected in the subarctic region and another bog in the boreal region. These sites have different geographical, climatological and ecological features (e.g. pH, nutrient availability, hydrology and species composition). Five cores from those three sites were dated using 210Pb and 14C. Loss on ignition and plant macrofossils analysis were performed for each core. First, we compare the simulation results of the HPM for the study sites with the information earned in the field and laboratory. In order to capture the causes for discrepancies between simulated and observed data, we then constrained the model in two ways: 1) The water balance of HPM was forced with water table fluctuations reconstructions, obtained from a transfer function of Testate amoebae. 2) The bulk density of HPM was forced with the bulk density data obtained from the cores. In both cases, the results highlight the effectiveness of the water balance and the bulk density routines of the HPM and also draw attention to other potential causes of inaccuracy in the model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".