Seasonal hydrology shifts production sources supporting fishes in rivers of the Lower Mekong Basin
Bibliographic record
Abstract
Seasonal hydrology is assumed to be an important reason why the Lower Mekong Basin supports highly productive and biodiverse inland fisheries. We used C and N stable isotope ratios of tissue samples to estimate primary production sources supporting fish biomass in the Mekong and three large tributaries in Cambodia. We used a Bayesian mixing model to estimate relative contributions of four alternative production sources — seston, benthic algae, riparian grasses, and riparian macrophytes. There was little seasonal variation in isotopic signatures of riparian plants, but benthic algae and seston showed large seasonal shifts in carbon ratios. Seston and benthic algae were the most important production sources supporting fish biomass overall during the dry season, and riparian vegetation was the most important source during the wet season. Sources contributed differentially to biomass of trophic and habitat guilds, especially during the dry season. A dam on the upper Sesan River has changed hydrology, channel geomorphology, and other factors and, compared with the other three rivers, its fish biomass appears to derive from algae to a greater extent.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".