Bibliographic record
Abstract
Biomonitoring is a common method to monitor environmental change in river ecosystems, a key advantage of biomonitoring over snap-shot physicochemical monitoring is that it provides a more stable, long-term insight into change that is also effects-based. In New Zealand, the main biomonitoring method is a macroinvertebrate sensitivity scoring index, with little established methods available for biomonitoring of fish. This study models the contemporary distribution of common freshwater fish and then uses those models to predict freshwater fish assemblages for each river reach under reference conditions. Comparison of current fish assemblages with those predicted in reference conditions (as observed/expected (O/E) ratios) may provide a suitable option for freshwater fish biomonitoring. Most of the fish communities throughout the central North Island and lower reaches show substantial deviation from the modelled reference community. Most of this deviation is explained by nutrient enrichment, followed by downstream barriers (i.e. dams) and loss of riparian vegetation. The presence of modelled introduced species had relatively little impact on the presence of the modelled native fish. The maps of O/E fish assemblage may provide a rapid way to identify potential restoration sites.
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 | 0.003 |
| 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) | 0.003 | 0.001 |
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".