Adjacency to a harvest trail increases drought resistance of interior Douglas-fir (<i>Pseudotsuga menziesii</i> var. <i>glauca</i>) in partially harvested stands in central British Columbia
Bibliographic record
Abstract
Drought tolerance of trees may be affected by competition, but most studies quantifying the relationship do not consider the effect of stem clustering. Trees are often clustered in interior Douglas-fir (Pseudotsuga menziesii var. glauca (Mayr) Franco) forests near the grassland interface in central British Columbia due to past harvesting practices or habitat management for mule deer (Odocoileus hemionus hemionus (Rafinesque, 1817)). Climate change projections indicate continued increases in temperature, an outcome that would stress trees growing in dry environments. Trees placed in different states of competition by mechanical harvesting in the 1970s were sampled to provide a 40-year comparison of three levels of competition during 1–2 year droughts. Tree-ring analysis was used to assess the reduction in growth during drought years and resumption of growth in subsequent years. A clear separation of growth rates was evident between open-growing trees, trees on the edge of harvesting trails, and trees within the unharvested interior. Edge trees had intermediate growth rates but no differences were found in the long-term climate–growth relationship compared with open-growing trees. Both Edge and Open classes showed less relative growth reduction during droughts than Interior trees growing between harvest trails. Precipitation throughfall rates and competition for resources are likely driving short-term drought tolerance in combination with other factors.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".