Vegetation phenology can be captured with digital repeat photography and linked to variability of root nutrition in<i><scp>H</scp>edysarum alpinum</i>
Bibliographic record
Abstract
Abstract Question Can repeat (time‐lapse) photography be used to detect the phenological development of a forest stand, and linked to temporal patterns in root nutrition forHedysarum alpinum(alpine sweetvetch) an important grizzly bear food species? Location Eastern foothills and front ranges of theRockyMountains inAlberta,Canada. The area contains a diverse mix of mature and young forest, wetlands and alpine habitats. Methods We deployed six automated cameras at three locations to acquire daily photographs at the plant and forest stand scales. Plot locations were also visited on a bi‐weekly basis to record the phenological stage ofH. alpinumand other target plant species, as well as to collect a root sample for determination of crude protein content. Results Repeat photography and image analysis successfully detected all key phenological events (i.e. green‐up, flowering, senescence). Given the relation between phenology and root nutrition, we illustrate how camera data can be used to predict the spatial and temporal distribution and quality of a key wildlife resource. Conclusions Repeat photography provides a cost‐effective method for monitoring vegetation development, food availability, and nutritional quality at a forest stand scale. Since wildlife responds to the availability and quality of their food resources, detailed information on changes in resource availability helps with land‐use management decisions and furthers our understanding of grizzly bear feeding ecology and habitat selection.
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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 | 0.001 | 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".