Bioassessment of benthic macroinvertebrates in wetlands: a paired comparison of two standardized sampling protocols
Bibliographic record
Abstract
Abstract We compared a rapid bioassessment protocol (Traveling Sweep Approach [TSA]) with a more conventional time intensive protocol (Composite Transect Approach [CTA]) to describe macroinvertebrates in wetlands in Alberta, Canada. We collected one macroinvertebrate sample using each protocol from 16 wetlands and compared abundance, catch per unit effort, and relative abundance between sample protocols. We also quantified and compared the logistics required to implement each protocol. The macroinvertebrate communities differed statistically between protocols for all three response variables; however, the differences were generally small and communities similar. The CTA protocol tended to yield higher variability in the samples, likely driven by the way these samples are collected and composited, which may introduce an unwanted source of variation when the primary monitoring objective is to assess effects of human activities over time and between sites. The CTA protocol also required significantly greater investment of time (ca. 50% greater processing time), money (ca. 1.9 times sample processing cost), and resources to execute (e.g., requirement for watercraft). Both protocols provided adequate characterization of macroinvertebrate communities in wetlands, but differences in variability and resources for deployment and processing are important considerations when choosing a sampling protocol. The rapid time-limited sweep protocol (TSA) appears to be a viable monitoring approach given that macroinvertebrate communities identified by each protocol were relatively similar but were collected using the TSA protocol at a lower cost.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".