Quantitative modelling of fish habitat in a large regulated river in a changing climate
Bibliographic record
Abstract
Abstract The expansion of hydropower in combination with the already existing infrastructure and a changing climate are significantly influencing the world's rivers. The resulting alteration in flow regimes is expected to strongly affect fish habitat and associated fish communities both spatially and temporally. Using habitat modelling, this study identified habitat bottlenecks during the critical summer low flow period and fish assemblages that will be most susceptible to regulated and predicted future flow regimes in the Saint John River downstream the Mactaquac Generating Station. Expert knowledge‐based habitat models were applied at the meso‐scale to evaluate the influence of alternative future flow regimes on habitat suitability indices of fish assemblages. Dam renewal and removal scenarios predicted low habitat suitability for rheophilic fish species, particularly during prolonged low flow periods in dry years. Limnophilic and eurytopic fish assemblages were not expected to be limited in habitat conditions. Overall, the proposed modelling approach represents a promising tool to support the development of environmental flows in large regulated rivers that face challenges with ageing infrastructure and a changing climate.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".