Development and application of benthic algal reference condition models to assess stream condition in the South Nahanni Watershed
Bibliographic record
Abstract
Abstract Monitoring biologists continually strive to improve the effectiveness of protocols to quantify environmental and ecological effects of anthropogenic activities. We developed and applied a reference condition approach (RCA) model to assess the ability of 3 descriptors of algal community structure (algal taxonomy, diatom taxonomy, and algal pigments) to identify impairment in 2 northern rivers in the South Nahanni River Watershed, Northwest Territories, Canada. We established reference conditions by sampling 62 regional reference (i.e., minimally disturbed) sites in 2008 (n = 44) and 2009 (n = 18) and assessed the condition of 38 test sites downstream of 2 mines in 2008 (N = 20 sites) and 2009 (N = 18 sites). Patterns of impairment downstream of the 2 mines were assessed and zones of influence were identified for each algal descriptor. Results showed that the 3 RCA models using the 3 descriptors of algal community structure identified reasonably consistent assessments downstream of Prairie Creek mine with changes in algal pigments being more sensitive than the other 2 descriptors. In Flat River, however, assessment of test sites varied considerably depending on the descriptor of algal community structure. Our results suggest that benthic algal RCA models show promise as biological monitoring tools, but additional investigations are required to better understand variance in site assessments among the 3 algal community descriptors. Integr Environ Assess Manag 2017;13:728–745. © 2016 SETAC Key Points Results showed that the 3 reference condition approach (RCA) models using the 3 descriptors of algal community structure identified reasonably consistent assessments downstream of 1 mine, with changes in algal pigments being more sensitive compared to the other 2 descriptors. Downstream of a second mine, however, assessment of test sites varied considerably, depending on the descriptor of algal community structure. Our results show that benthic algal RCA models have promise as biological monitoring tools, but additional investigations are required to better understand variance in site assessments among the 3 algal community descriptors. We believe that presenting data that shows promise but also providing results that do not work as well is helpful in moving knowledge of RCA models forward.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".