Multiaged redwood responds well to partial harvest and herbicide treatments
Bibliographic record
Abstract
Chemical control of unwanted trees can be a cost-efficient tool for forest management and restoration. In California, United States, the response of merchantable conifers to hardwood control is poorly understood. We studied the tree growth of coast redwood (Sequoia sempervirens (Lamb. ex D. Don) Endl.) following herbicide frill treatment of competing tanoak (Notholithocarpus densiflorus (Hook. & Arn.) Manos, C.H. Cannon, & S. Oh), coinciding with a partial harvest of conifers. The radial growth of 420 redwoods in 45 plots was measured using increment cores. With or without partial harvesting, herbicide treatment of tanoak enhanced growth of most redwoods: 23% of redwoods in herbicide-only plots and 34% of redwoods in herbicide + harvest plots had ≥100% higher posttreatment basal area increment (BAI). In untreated plots, 67% of redwoods displayed declining BAI. The response of redwoods (the ratio of 8-year postharvest BAI to 8-year preharvest BAI) was 59% higher in herbicide-only plots and 108% higher in herbicide + harvest plots compared with untreated control plots over the same period. Redwoods with long crowns maintained rapid growth with or without treatment. Trees growing slowly before treatment exhibited the greatest response, provided that they had relatively long crowns and were not left in suppressed crown positions. Forest managers implementing partial harvesting and (or) chemical control of hardwoods can expect to maintain or promote rapid growth of most residual redwoods.
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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".