Data from: Depth-to-water mediates bryophyte response to harvesting in boreal forests
Bibliographic record
Abstract
1. Site moisture is an important component of the forest landscape for maintaining biodiversity, including forest-floor bryophytes, but little is known about its role in shaping understory responses to harvesting. 2. We investigated the influence of site wetness, determined using a remotely-sensed, topographic depth-to-water (DTW) index, on responses of bryophyte cover, richness, diversity, and composition to variable retention harvesting (comparing: 2% [clear-cut], 20%, and 50% dispersed green tree retention and uncut controls [100% retention]) in three boreal forest cover-types (broadleaf, mixed, and conifer forests) in western Canada. The DTW index provides an approximation of depth to water at or below the soil surface, and was derived from wet-areas mapping based on discrete Airborne Laser Scanning data acquired over an experimentally harvested landscape located in northwestern Alberta, Canada. 3. The effectiveness of leaving retention (versus clear-cutting) for conserving bryophyte communities depended on site wetness, as indicated by DTW, with the specifics varying among forest types. In broadleaf forests, bryophyte cover and richness were generally low and not much affected by harvesting but drier sites had higher richness and a few more unique species. In mixed and conifer forests, leaving retention (versus clear-cutting) on wetter (versus drier) sites was more effective for conserving bryophyte cover, wetter sites had higher total species richness, and more species were exclusive to wetter sites. 4. Synthesis and applications. Site wetness, as indicated using the remotely-sensed topographic site wetness index "depth-to-water," mediates bryophyte responses to variable-retention harvests. Specifically, our results suggested that in conifer and mixed forests it would be more beneficial to target wetter sites for retention patches or dispersed retention whereas in broadleaf sites there might be a slight advantage to targeting drier sites. Our study demonstrates that this tool could be used to inform management decisions around leaving dispersed or patch retention.28-Jan-2019
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.006 |
| 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".