Assessing spatial variation in browsing history by means of fraying scars
Bibliographic record
Abstract
Abstract Aim We used fraying scars to understand spatial variation in browsing history. Information on browsing history is an essential background in studies on the long‐term effect of deer browsing on the flora and fauna and of its variation in space. Location We focused on two small neighbouring islands of Haida Gwaii (British Columbia, Canada), Reef Island and South‐Skedans Island, colonized by introduced black‐tailed deer ( Odocoileus hemionus sitkensis ). Methods We searched for sites where trees with fraying scars were clustered. We studied the trees that deer selected (species, size) and the characteristics of scars (number, position, size). Using a cross‐dating procedure, we dated fraying scars with dendrochronology, obtaining an accurate estimate of the year the scar was formed. Results On Reef Island, Thuja plicata was the tree species chosen for fraying. On South‐Skedans Island, where Thuja plicata is missing, deer chose Salix sp. and Alnus rubra . Deer chose only trees with a circumference of less than 50 cm. About two to three fraying scars were recorded per tree. All of them extended between 30–40 and 70–80 cm from the ground and were between 5 and 6 cm in width. On Reef Island, 95% of the scars were formed during the last 50 years. On South‐Skedans Island, 95% were formed over the last 10 years. Age distribution of scars showed a constant increase of the number of scars over time. It indicated that deer had colonized Reef Island 53 years prior to this study but were absent or rare on South‐Skedans Island until 13 years prior to this study. Main conclusions These results indicate different colonization dates and thus different length of browsing histories for the islands studied and provide the historical background necessary to analyse the involvement of deer in the current differences in the flora and fauna observed between islands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".