Early response of <i>Nothofagus antarctica</i> forests to thinning intensity in northern Patagonia
Bibliographic record
Abstract
Nothofagus antarctica (G. Forst.) Oerst. stands of northern Patagonia (Argentina) have great potential to provide multiple ecosystem services. Nonetheless, the lack of basic information limits the application of silvicultural treatments to this forest type. This study reports the early response to three systematic strip thinning treatments (30%, 50%, and 70%) carried out during 2013 in a 30-year-old N. antarctica stand, where control plots (i.e., no thinning) were also established. Subplots were located within each plot. Basal diameters (BD) of all retained stems were measured in the non-vegetative season (winter) of 2016, 2017, and 2018 to calculate basal diameter annual increment (BDI) and stand volume growth. BDI ranged from 1.9 mm·year−1 in the control plots to 2.7 mm·year−1 at 70% thinning. Relative BDI also responded positively to thinning intensity. Stand volume growth decreased non-linearly with thinning intensity from 3.36 m3·ha−1·year−1 in the control plots to 0.71 m3·ha−1·year−1 when thinning was 70%. Smaller differences were observed between control plots, 30% thinning, and 50% thinning. Our results show that N. antarctica forests, in the stem exclusion stage, respond to strip thinning. Thus, silviculture based on this management system could favor the development of retained trees without losing productivity if it remains under 50% intensity. This outcome represents an important incremental contribution to the design of silvicultural interventions in northern Patagonia.
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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".