The effect of a north-facing forest edge on tree water use in a boreal Scots pine stand
Bibliographic record
Abstract
Tree water use and growth increment were studied in a north-facing forest edge of a 70-year-old monospecific Scots pine (Pinus sylvestris L.) stand growing on poor sandy soils in the northern boreal zone of Sweden. The forest edge in this study bordered a 20-year-old clearcut. There were differences in water use and growth increment during the growing season between trees growing at the forest edge and trees growing in the forest interior. These differences were likely related to soil conditions, such as access to soil moisture, soil temperature, and soil frost conditions, whereas an effect of aboveground microclimate was not found. The estimated tree water use and growth increment over one growing season tended to be greater for trees at the edge zone relative to those from the interior. The variability of the measured tree water fluxes was high, especially for the edge-zone trees. There were also structural differences between the two groups of trees, most notably in the radial profile of conductive xylem, in tree height, and in green crown length, but these differences were on the limits of statistical significance. The estimated seasonal transpiration was low, about 70 mm when estimated exclusively for trees in the forest interior and 107 mm when estimated exclusively for trees at the forest edge. This illustrates the likely magnitude of water use enhancement resulting from the conditions specific to the forest edge.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".