Bibliographic record
Abstract
Abstract. As drought and prolonged water stress become more prevalent in dry regions under climate change, understanding and preserving water resources has become the focal point of many conversations. Forest regeneration after deforestation or disturbance can lead to over-populated juvenile stands with high water demands and low water use efficiency. Forest thinning improves tree health, carbon storage, and water use while decreasing stand demands in arid and semi-arid regions. However, little is known about the impacts of over-population on seasonal variation in depth to water uptake nor the magnitude of the effect of growing season drought conditions on water availability, and existing reports are highly variable by climatic region, species, and thinning intensity. In this study, stable isotope ratios of hydrogen (δ2H) and oxygen (δ18O) in water collected from soil varying depths and from twigs of lodgepole pine (Pinus contorta) under different degrees of thinning (control: 27,000 stems per ha; moderately thinned: 4,500 stems per ha; heavily thinned: 1,100 stems per ha) over the growing season and analyzed using the MixSIAR Bayesian mixing model to calculate the relative contributions of different water sources in the Okanagan Valley in the interior of British Columbia, Canada. We found that lodgepole pine trees shift their depth to water uptake depending on water availability under drought conditions and rely more heavily on older precipitation events that percolate through the soil profile when shallow soil water becomes less accessible. Interestingly, forest thinning did not cause a significant change in depth to water uptake. Our results support other findings by indicating that although lodgepole pines are drought tolerant and have dimorphic root systems, they cannot shift from deep water sources when shallow water becomes more available at the end of the growing season.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.009 |
| Insufficient payload (model declined to judge) | 0.432 | 0.311 |
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".