Are bioassessments based on the Reference Condition Approach (RCA) affected by 'rapid' approaches to sample collection and processing?
Bibliographic record
Abstract
Benthic invertebrates are used by a number of agencies worldwide as indicators for assessing stream health, which has resulted in the development of a variety of protocols for collecting and processing benthic samples. The large number of methods used means that calibration of data collection is not always possible, but if different methods produce similar estimates of community composition and metric values, then sharing of data can make bioassessments more efficient. This study explored the effect of two approaches to subsampling and sorting of benthic invertebrates on community composition, calculation of metrics, and assessment of stream health. We compared two commonly used sampling methods: a rapid approach, employing live, unaided sorting and a standard approach using microscope sorting of preserved samples, through a comparison of replicate samples collected from 61 streams. This study found that both methods resulted in similar estimates of community composition at a site, as determined by the Bray-Curtis similarity index. However, the live sorting methodology resulted in greater family richness and higher estimates of metrics that reflect large taxa (i.e., %EPT). Despite differences in a number of metrics, both methods performed equally well at identifying impairment in the test sites, with live-sorting samples slightly more sensitive.
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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 | 0.162 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".