Bridging Traditional and scientific knowledge through a novel predictive approach to understand the role of pathogens in the decline of a key Arctic species
Bibliographic record
Abstract
The main objective of this project is to understand and quantify the potential role of parasites and pathogens in population dynamics and declines of barren-ground caribou (Rangifer tarandus groenlandicus) through ecological modeling and Indigenous observation and monitoring. We hypothesized that the costs of diseases will have a negative impact on caribou physiology and, as a direct consequence, a negative impact on caribou population dynamics. This is likely to be a combination of cumulative effects through subtle energetic mechanisms such as decreasing body condition and pregnancy rates, as well as direct mortality, fetal abortion, or infertility. We built a bio-energetic integral projection model to evaluate the impact of parasites and biting insects on caribou body condition, and as a result, caribou population dynamics. Results from this model demonstrate the significant impact that sub-lethal infection and behavioural modifications caused by parasites and insect pests can have on caribou. We then parameterized the model with environmental data from the Bathurst herd’s range between 1980 and 2020, when population declines occurred. This parameterization resulted in a greater than 50% population decline. While this did not perfectly capture observed declines of the Bathurst herd, it demonstrates the contribution that parasites and insect pests may have played. These results are feeding back into monitoring by directing efforts of Ekwǫ̀ Nàxoèhdee K’è to focus on caribou health, and the modeling framework can be used to forecast possible demographic trends under anticipated environmental conditions. This database includes data objects with results from baseline analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".