Spatial pattern in the organic layer and tree growth: A case study from regenerating <i>Picea mariana</i> stands prone to paludification
Bibliographic record
Abstract
Abstract Questions: 1. How does the spatial structure of the organic layer affect tree sapling physiology? 2. Are the organic layer and Picea mariana height growth spatially structured at different scales? 3. Does microtopography influence the accumulation of organic matter and does organic layer thickness affect height growth? Locations: Picea mariana forests, northwestern Quebec, Canada. Methods: We assessed the spatial pattern of each variable in one wildfire site and one harvest site using semivariograms and correlograms. We measured the cross‐correlation between relative elevation and organic layer thickness, and between organic layer thickness and growth using cross‐correlograms. Results: Picea mariana height growth was autocorrelated to a greater extent in the wildfire site (103 m) than in the harvest site (43 m). The spatial structure of organic layer thickness was similar in both sites. Deeper depressions in the harvest site, as illustrated by spatial variance in relative elevation at short distances (ca. 50 m), and by high autocorrelation values, increased the accumulation of organic matter within 20 m. Conclusions: The interaction between microtopography and organic matter accumulation led to paludification and poor growth of Picea mariana at the harvest site. Paludification at the wildfire site was independent of microtopography and was probably a result of stand development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".