Linking microtopography with post‐fire succession in bogs
Bibliographic record
Abstract
Abstract Questions: Does post‐fire plant succession in boreal bogs vary microtopographically and are successional patterns reproducible among similar microtopographic features? Does succession preserve microtopography post‐fire? Location: Boreal bog peatlands near Sinkhole Lake and Athabasca, Alberta, Canada. Methods: We assessed microtopographic variation in post‐fire plant community succession through stratigraphic macrofossil analysis of bog soil cores collected from high (hummock) and low (hollow) positions. We conducted vegetation surveys and collected soil cores from ten hummocks and hollows in each bog. Pre‐fire microtopographic status was inferred based on floral composition and compared to current microtopography. Results: Hollow vegetation was more variable than hummocks, both in present composition and post‐fire succession. The successional trajectory of current hummocks was relatively uniform, showing relatively rapid shifts to Sphagnum fuscum dominance, but varied greatly in hollows. Hollows, although compositionally variable, were typically perpetuated following fire, while hummocks had an approximately equal chance of being perpetuated or becoming hollows. Conclusions: Greater compositional variability at lower microtopographic positions, both spatially and temporally, is most likely due to the ability of hollows to support a wider range of species and greater susceptibility to severe disturbance. Likewise, spatial variability in fire severity appears to be responsible for perpetuation or change in microtopographic status, favouring the creation of hollows over maintenance of hummocks.
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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 | 0.000 | 0.000 |
| 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".