Spatial and temporal gradients in artisanal fisheries of a large Neotropical reservoir, the Itaipu Reservoir, Brazil
Bibliographic record
Abstract
Physical, chemical, and biological gradients along reservoirs are clear and exhibit marked spatial and temporal variations. These variations are rarely quantified and may produce spatial gradients in fisheries. We analyzed trends in total yield and gradients and relationships between catch-per-unit-effort (CPUE) and some characteristics of the fishery in the large Neotropical Itaipu Reservoir in Brazil. Data on the artisanal fisheries were collected over an 11-year period (414 213 daily trips). Annual yield (especially after 1993) and CPUE (annual total and for each species) decreased over the studied period. A clear longitudinal pattern in the CPUE values for the main species was recognized, and this pattern presented a significant relationship with the type of gear and characteristics of the vessels used in the fisheries. The decline in yield and CPUE is apparently due to changes in trophic state, as well as to the construction of reservoirs upstream from the region and to overfishing. It is clear that the spatial zonation influenced fish species distribution along the reservoir and, therefore, the fishery. We conclude that for this large Neotropical reservoir, spatial gradient cannot be ignored in management plans, and this appears to be true for any reservoir that exhibits zonation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".