Influences of gap microheterogeneity on the regeneration of <i>Nothofagus pumilio</i> in a xeric old-growth forest of northwestern Patagonia, Argentina
Bibliographic record
Abstract
We experimentally examined the influences of within-gap environmental heterogeneity on regeneration patterns of Nothofagus pumilio (Poepp. & Endl.) Krasser near the xeric limit of its distribution in northern Patagonia, Argentina. Results from this xeric old-growth forest are compared with patterns previously described for the same species in mesic forests. Survival of N. pumilio seedlings beneath tree-fall gaps in this relatively xeric forest appears to be strongly influenced by moisture availability. Seedlings and saplings that have survived this demographic bottleneck are found at microsites where soil water potentials are higher, such as in the shady northern edges of tree-fall gaps (Ψ = -0.46 MPa compared with less than -0.6 MPa in other gap positions) and on coarse woody debris (Ψ = -0.29 MPa, compared with -0.51 MPa on the forest floor). Although gap creation in this dry N. pumilio forest is favorable to tree regeneration by releasing light resources, decreased water resources may switch the system from a light- to a water-limited system in some positions of the gap. This may explain the lack of regeneration of N. pumilio often observed after creation of large gaps towards the xeric end of its range and needs to be considered in the management of this important timber species.
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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 | 0.000 | 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) | 0.001 | 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".