Bibliographic record
Abstract
Peatlands which are valued for their long-term carbon storage are subject to climate change impacts. Many of the mechanisms regulating carbon flux in peatlands are not well understood. Studies have hypothesized that spatial heterogeneity and patchiness of vegetation associated with micro-topography and hydrological gradients within peatlands play an important role in controlling trace gas exchange. Following this hypothesis, the objectives of my research is to characterize the spatial distribution of vegetation properties and water table level linked to hummock and hollow structures using ground data and remote sensing at the Mer Bleue peatland in Ontario. As well as determine whether these two methods will provide the same information with respect to the spatial patterns of vegetation. A hierarchically nested cyclic sampling scheme of leaf area index (LAI), percent vegetation cover and water table level provided concrete measures of peatland properties for spatial analysis. However, ground surveys covering large areas are time-consuming, expensive, and can damage peatland vegetation through trampling and repetitive sampling. Therefore, this research combined localized ground verification surveys with broad multi-spectral high resolution (2.44m) QuickBird satellite information to quantify spatial heterogeneity. Geostatistical analysis was performed on data from each of these sources to explore the spatial dependence of ground data and spectral reflectance. For the ground data, these analyses produced ground survey range results that mainly varied between 2 and 4m. These were interpreted to represent groupings of multiple 1m hummock structures or lawn (a flatter, wider hummock) structures observed in the field. The sill height demonstrated a linear relation to vegetation cover. The relative nugget error (RNE) showed variability occurring at scales finer than 1m likely due to the individual hummock structure, canopy layering and vegetation niche range. The remotely
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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".