Does site quality affect the additive basal area phenomenon? Results from Chilean old-growth temperate rainforests
Bibliographic record
Abstract
Complex old-growth forests with large emergent trees may support more basal area than those without these trees. The so-called additive basal area phenomenon occurs when the presence of these emergent trees does not affect the basal area of the canopy trees, i.e., the basal areas of emergent and canopy trees add up. We studied two old-growth forests in south–central Chile with similar species composition and structure but contrasting soil quality. We sampled fifty-seven 1000 m2 plots in a relatively high-quality site (Quillaipe) and sixty-five 1000 m2 plots in a relatively low-quality site (Yaldad) and evaluated total stand basal area, basal area of emergent trees, basal area of canopy trees, and the influence of the basal area of emergent trees on total and canopy basal areas. On average, Quillaipe accumulated 50% higher basal area than Yaldad. The additive basal area effect only occurred on the high-quality site, Quillaipe. A more detailed analysis showed that canopy associates were differentially affected in terms of basal area depression with increasing basal area of emergent trees in each site. The relative competitive ability of canopy species, as affected by their ecological traits and geographical distribution, presumably explain if the basal area of individual species or functional groups are depressed by the basal area of emergents. Above- and below-ground resource partitioning may be decisive mechanisms affecting the occurrence of additive basal area accumulation in forest stands.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".