The influence of the root diseases <i>Armillaria solidipes</i> and <i>Inonotus sulphurascens</i> on the distribution of mule deer during winter
Bibliographic record
Abstract
Abstract Armillaria (Armillaria solidipes) and laminated root diseases (Inonotus sulphurascens) are two wide-ranging fungal pathogens that occur in the southern half of British Columbia (BC), Canada, and can infect economically and biologically important tree species such as interior Douglas-fir (Pseudotsuga menziesii var. glauca). In northern, temperate locations, Douglas-fir forests serve as winter habitat for ungulates. When these fungal infections are in Douglas-fir forests, core components of winter ranges are altered, including canopy cover, snowpack and understory vegetation. In this study, we investigated how Rocky Mountain mule deer (Odocoileus hemionus hemionus) of central BC used winter range habitats that included root disease (A. solidipes and I. sulphurascens) centres. We used remote camera-traps to collect data from September 2017 to April 2019, and we assessed those habitats in which the cameras were located during the summer and winter of 2018. We used logistic regression and an information theoretic approach to test a series of factors hypothesised to influence the use of root disease centres by mule deer. Our results show that mule deer use root disease centres less than control forests as well as negatively respond to the deeper snow packs found in root disease centres, especially in late winter. Our cameras also detected higher vertebrate diversity in root disease centres. We suggest that forest policy-makers should acknowledge heterogeneous habitat features such as root disease centres within ungulate winter ranges and consider adjusting estimates of habitat capability for deer based on our findings.
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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 | 0.001 |
| 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".