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.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".