The impact of the timing of brush management on the nutritional value of woody browse for moose<i>Alces</i><i>alces</i>
Bibliographic record
Abstract
Summary We examined how the removal of above‐ground biomass (mechanical brushing) at different times of the year affected the nutritional value of regenerating shoots of Scouler’s willowSalix scoulerianafor moose for two winters after brushing. Brushing trials were conducted throughout the 1996 and 1997 growing seasons in central British Columbia on a 10‐year‐old regenerating clear‐cut replanted in lodgepole pinePinus contortavar.latifolia. We assessed the nutritional value of the browse in relation to length, diameter, mass, digestible energy, digestible protein, tannin and lignin content of current annual growth shoots in winter, as well as the phenology of plant leafing. One winter after brushing, willows brushed in early July had shoots that were lower in lignin, higher in digestible protein and lower or not different in tannin content compared with shoots from earlier brushed or unbrushed willows. Willows brushed in early July also had long, heavy, shoots that were high in digestible energy and delayed leaf senescence. In the second winter after brushing, willows that were brushed in July had larger shoots that were lower in digestible energy, digestible protein, tannin and lignin content and delayed leaf senescence compared with several other treatments. Willows brushed after July regenerated negligible shoot material in the first year after brushing. Willows brushed in September delayed leaf flush in the first post‐brushing spring. To increase the nutritional value of woody browse for cervids, we suggest that brushing should be performed in early to mid‐July (mid‐summer). Reductions in browse quality and quantity may negatively affect many mammalian species. Therefore, we recommend that the needs of other fauna potentially affected by changes in shrub architecture, shoot morphology and shoot chemistry be considered when planning the timing of brush management activities.
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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 | 0.001 | 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".