Spatial characteristics of canopy disturbances in riparian old-growth hemlock – northern hardwood forests, Adirondack Mountains, New York, USA
Bibliographic record
Abstract
Gap dynamics in temperate, late-successional forests influence important riparian functions, including organic matter recruitment and light environments over streams. However, controls on gap dynamics specific to riparian forests are poorly understood. We hypothesized that (i) gaps are larger and more frequent nearer streams, (ii) gaps cluster at within-stand scales, and (iii) tree damage type and gap fraction vary among riparian landforms. All gaps within four 6–9 ha plots in riparian old-growth eastern hemlock ( Tsuga canadensis (L.) Carrière) – northern hardwood forest in the Adirondack Mountains, New York, USA, were mapped and measured. We recorded species, damage type, and diameter at breast height for gapmakers and dominant perimeter trees. Spatial distribution was assessed with Ripley’s K. Spatial autocorrelation in gap area and tree damage type were assessed using Moran’s I. Linear regression analysis defined relationships between proximity to streams and gap area and frequency. Expanded gap fraction ranged from 28.3% to 47.6%. Gaps were randomly distributed at scales ≤25 m and clustered at scales of 63–122 m. Distribution patterns were not consistent at other scales. Convergent and divergent landforms significantly influenced gap fraction, tree damage type, and species distributions. Positive correlations between convergent topography and gap area suggest an interaction between low-order riparian landforms and gap formation dynamics in late-successional forests.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".