The river runs through it: Evaluation of the effects of agricultural land use practices on macroinvertebrates in Prince Edward Island streams using both new and standard methods
Bibliographic record
Abstract
Thirty-seven streams representing reference and test sites were sampled for the benthic macroinvertebrate fauna and habitat characteristics during two seasons (summer and fall) in 2001. Several reference condition approaches were used to evaluate the streams. The BEnthic Assessment of SedimenT was used to group related streams based on the macroinvertebrate community composition (which was related mostly to size characteristics of the stream). Biological (taxa abundance) and environmental characteristics were considered simultaneously to produce a prediction model for a reference condition for PEI. The model was also used to predict the kinds and abundance of various taxa that should occur in a site, based on habitat characteristics. Eleven “'test” samples (from sites which were believed to be impacted by agriculture) were then compared to the reference sites with similar habitat characteristics to assess whether their macroinvertebrate fauna matched the predictions. The same groups of sites were compared using nine biological metrics, which compare the abundance or proportions of certain taxa or groups of taxa. These methods were also used to assess the Wilmot River after a catastrophic pesticide runoff event in the summer of 2002. (Abstract shortened by UMI.).
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".