Spatial heterogeneity of lodgepole pine sapling densities following the 1988 fires in Yellowstone National Park, Wyoming, USA
Bibliographic record
Abstract
Large disturbances create spatial heterogeneity in vegetation re-establishment, and documenting such variability is critical for understanding and predicting succession. We quantified the spatial heterogeneity of lodgepole pine sapling densities 10 years after the 1988 fires in Yellowstone National Park using color infrared orthophotographs. Densities were classified across the landscape at an accuracy of 70.9%, and landscape metrics were used to characterize their spatial variability. Densities ranged from 0 to > 500 000 saplings/ha, but >60% of the burned area was represented by densities <5000 stems/ha. The burned area consisted of small patches averaging 1.5 ha in area at a mean patch density of 68 patches/100 ha. Densities occurred in nearly equal proportions across the landscape (Shannon's evenness = 0.85) but were well dispersed (contagion index ≈ 20%), suggesting that densities varied in a complex, fine-grained mosaic across the landscape, with high-density patches occurring within a matrix of larger, lower density patches. High-density patches were similar in area to severe surface fires, suggesting that burn severity is an important explanatory variable for spatial variation in sapling density. Large, stand-replacing fires may result in heterogeneous forest landscapes rather than homogenous forests of uniform structure, which may have important consequences for postfire ecological processes.
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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 | 0.000 | 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 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".