Vegetation structure and soil organic carbon storage across northern forest-tundra ecotones in continuous permafrost
Bibliographic record
Abstract
Climate change is altering northern vegetation structure and below-ground carbon storage. Expanding forest and shrub cover has decreased soil organic carbon (SOC) storage in some parts of the forest-tundra ecotone. In this study, we linked measurements of SOC with terrain and vegetation structure derived from drone imagery across treelines underlain by continuous permafrost in the Northwest Territories, Canada. We classified sites into three treeline types representing differences in vegetation productivity and topography. Between treeline types, we observed differences in C:N ratios and organic matter depth related to the rate of soil carbon turnover and SOC storage. Overall, SOC showed small positive relationships with tree stem density and average canopy height. We did not find evidence that expanding tree- and shrublines would result in losses of SOC storage in our study area. Instead, topography and landscape drainage patterns, rather than vegetation structure may be more important predictors of SOC storage. We used medium resolution satellite data to extend predictions of treeline type across our study area. The majority of predicted treelines (82%) showed positive relationships between vegetation height and SOC storage. Our findings highlight the value of integrating vegetation structure and landscape features in understanding carbon dynamics in the forest-tundra ecotone.
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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) | 0.000 | 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".