Human disturbance-related contamination in First Nation food and medicinal plants determined through a comparison of multivariate frequentist and Bayesian regression analysis
Bibliographic record
Abstract
Increased development in forestry, oil and gas, road infrastructure, and agriculture sectors across the Swan River Watershed (Alberta, Canada) has led to an increase in the impact they have on the riparian and aquatic ecosystems. These industries require the removal of vegetation for construction and operation, and some do not require buffer zones around waterbodies to protect aquatic habitat. These industries also use herbicides and fertilizers that may contain high levels of heavy metals, as well as glyphosate. In this thesis, I examined the changes to abundance of plant species chosen by Knowledge Keepers of the Swan River First Nation, as well as general plant categories (i.e., shrub, trees, and herbaceous), in relation to industrial development. I also examined how heavy metal and glyphosate content changed in these plant species with distance from industrial development. Sixty-seven sites were sampled across the Swan River Watershed, each with seven transects examining plant abundance. Tissue samples of chosen species were collected on the first, fourth, and seventh transects. I found significant ( = 0.05) changes to the abundance of aquatic and riparian plant species and categories in association with industrial activity. I also found significant ( = 0.05) changes to heavy metal concentrations in response to industrial presence and distance. There was a significant ( = 0.05) increase in the presence of glyphosate in plant species when forestry or agriculture were present at a site. These results were obtained after conducting a comparison of multiple Bayesian and frequentist regression analysis. There has been an increased interest in Bayesian analysis in ecology, however there is still some hesitation around its implementation due to hardware and software costs, time, and education. The Bayesian method resulted in smaller root-mean-squared-errors and increased precision. I also found that the time and costs were the same as the frequentist analysis, when using a dataset collected over one field season. The biggest barrier in the implementation of Bayesian analysis was the lack of accessible education through formal university courses.,
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".