Injection-based approaches for controlling Douglas-fir (<i>Pseudotsuga menziesii</i>) invasion in conservation efforts of the Patagonian forest
Bibliographic record
Abstract
Abstract Invasion by nonnative woody species poses a major threat to the environment, biodiversity, and economies worldwide. Nahuel Huapi National Park in Argentina is a protected area for habitat conservation that harbors several invasive Pinaceae species, where Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] is one of the most aggressive and abundant conifer tree invaders. Management of invasions in protected areas must include efficient, easy to deploy, and cost-effective techniques, while reducing the impact on native ecosystems. Because the region has no control measures applied other than conventional felling, we analyzed the effectiveness of two systemic herbicides (glyphosate and aminopyralid + triclopyr) at two different concentrations, applied with the drill and fill method. We then quantified defoliation of P. menziesii trees 6, 12, and 24 mo after application and performed an economic cost analysis to determine profitability. For the application, the trees were grouped into diameter at breast height classes and randomly assigned to one of the four treatments. Herbicide doses were adjusted according to tree size. We found that glyphosate at high concentrations completely defoliated 33% of the trees after 6 mo and 87% after 12 and 24 mo. Glyphosate at low concentrations defoliated almost 30% of the trees after 24 mo, most of which were smaller trees. The aminopyralid + triclopyr treatment did not produce significant defoliation at any of the tested concentrations. When compared with conventional felling, the drill and fill method was found to reduce removal costs by 98%. We observe that differences in costs are mainly due to dead trees that remain standing, decompose slowly, and do not generate costs associated with their removal and debris management. Drill and fill is a suitable method for treating scattered trees in a native forest community, with reduced environmental consequences compared with other removal techniques currently applied within conservation areas of the Patagonian forest.
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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".