Paludification dynamics in the boreal forest of the James Bay Lowlands: effect of time since fire and topography
Bibliographic record
Abstract
In many northern forest ecosystems, soil organic matter accumulation can lead to paludification and forest productivity losses. Paludification rate is primarily influenced by topography and time elapsed since fire, two factors whose influence is often confounded and whose discrimination would help forest management. This study, which was conducted in the black spruce ( Picea mariana (Mill.) BSP) boreal forest of northwestern Quebec (Canada), aimed to (1) quantify the effect of slope and time since fire on paludification rates, (2) determine whether soil organic layer depth could be estimated by surface variables that can potentially be remotely sensed, and (3) relate the degree of paludification to tree productivity. In this study, soil organic layer depth was used as an estimator of the degree of paludification. Slope and postfire age strongly affected paludification dynamics. Young stands growing on steep slopes had thinner organic layers and lower organic matter accumulation rates compared with young stands growing on flat sites. Black spruce basal area and Sphagnum cover were strong predictors of organic layer depth, potentially allowing mapping of paludification degree across the landscape. Tree productivity was negatively related to organic layer depth (R2= 0.57). The equations developed here can be used to quantify forest productivity decline in stands that are undergoing paludification, as well as potential productivity recovery given appropriate site preparation techniques.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".