Detecting changes in forest floor habitat after canopy disturbance
Bibliographic record
Abstract
Abstract A massive ice storm hit northeastern North America in 1998, dropping more than 100 mm of freezing rain at its epicenter in southern Quebec, Canada. There has been extensive study of which trees and areas received the most damage, but the biodiversity consequences of this damage at landscape scales have not received much attention. We assessed the effectiveness of seven remotely sensed vegetation indices—Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index, Difference Vegetation Index, Renormalized Difference Vegetation Index, Atmospherically Resistant Vegetation Index, Green Normalized Difference Vegetation Index and Visible Atmospheric Resistant Index—for modeling the coarse woody debris (CWD) influx in an old growth forest reserve at the storm's epicenter; NDVI was the best predictor of CWD influx. We categorized the geospatial CWD predictions from the NDVI‐derived model to map the spatial distribution of sun‐exposed, moist‐shaded, dry‐shaded and wet CWD microhabitats on the forest floor. Moist‐shaded, dry‐shaded and wet patches of CWD were large and well connected, but sun‐exposed patches were small and sparse. Since these microhabitats affect the distribution and abundance of saproxylic insects, wood‐rotting fungi, salamanders, birds, small burrowing mammals and plant species dependent on nurse‐logs for establishment, the CWD influx from the 1998 ice storm may have revitalized local populations of these taxa through increased habitat availability as well as increased dispersal within the reserve.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".