Growth of balsam poplar and black cottonwood in Alaska in relation to landform and soil
Bibliographic record
Abstract
While constructing site index curves for balsam poplar (Populus balsamifera L.) and western black cottonwood (Populus trichocarpa Torr. & A. Gray) for interior and southcentral Alaska, we found variations in growth patterns that appeared to be related to landform and soil properties. We characterized soils for 42 of 65 site index plots in an attempt to explain site productivity variation. We found significant negative correlations between site index and elevation. Region, landform, and floodplain characteristics (especially sediment deposition) significantly affected poplar growth rate and soil development patterns. Nutrient availability and recycling appear to be mediated by flooding through scouring or burial of surface organic layers. Soil pH patterns related to O-horizon development and salt crust formation and dissolution described previously for the Tanana River floodplain do not hold for all floodplains in Alaska. At similar latitudes and elevations, upland locations may have higher site indices than frequently sedimented floodplain locations because upland soil development is relatively uninterrupted. Floodplain locations experiencing little or no sediment accumulation after establishment of poplar stands tend to have higher site indices than those experiencing frequent sediment accumulation. At some floodplain locations, site index was positively correlated with rooting depth.
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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".