Patterns of conifer establishment and vigor on montane river floodplains in Olympic National Park, Washington, USA
Bibliographic record
Abstract
In the Pacific Coastal Ecoregion, coniferous trees are often prescribed for riparian restoration, yet little is known about their establishment on floodplains under natural conditions. In this study, 10- to 50-year-old floodplain surfaces of six rivers were surveyed to (1) quantify conifer distribution along study reaches, (2) describe relationships between conifer presence and selected biological and environmental variables, and (3) compare growth rates and relative vigor of conifers among sites. We found conifers on 17%–36% of the plots we sampled. Sitka spruce ( Picea sitchensis (Bong.) Carrière) was most common on the wetter sites, while Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) was most common on the drier sites. Other conifers common to adjacent terraces were extremely rare. Douglas-fir preferred elevated sites with shallower soils and fewer hardwood competitors (e.g., Alnus rubra Bong. and Salix spp.) than similar plots without conifers. For Sitka spruce, the variables examined revealed no statistical differences between conifer and non-conifer plots. Our findings suggest that the tolerance of Douglas-fir to drier conditions allows it to survive on relatively higher, drier sites where more moisture-demanding competitors fail. For Sitka spruce, factors other than those measured appear to be more important in spruce establishment and survival.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".