Estimating shallow soil available water supply for Douglas-fir forests of the coastal Pacific Northwest: climate change impacts
Bibliographic record
Abstract
Douglas-fir forests of the coastal Pacific Northwest experience yearly summer droughts; however, the variation in shallow soil available water supply throughout the region is not well understood nor is the effect of future climate change. Soil moisture sensors were installed in 60 Douglas-fir plantation forests over 6 years. Stands were grouped by physiographic regions to describe differences in climate and available water supply. Monthly available water supply (MAWS) (0–50 cm) was calculated as the average daily available moisture content. MAWS was modeled using monthly climate variables, and the equation was then used to predict the change in MAWS due to mild, moderate, and severe climate change predictions. Regional monthly air temperature and precipitation were strongly predictive of MAWS. Mild to severe climate change are predicted to decrease yearly available water supply by 8% to 19%, while summer available water supply will decrease from 25% to 72%. The greatest decreases due to climate change will be found in the coastal regions of Washington and Oregon due to greater negative effects of temperature on available water supply. Climate change, especially the most severe predictions, was shown to have a sizeable effect on shallow soil available water supply in coastal Douglas-fir forests.
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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 | 0.001 |
| 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) | 0.000 | 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 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".