The effect of water velocity on stable carbon and nitrogen isotope signatures of periphyton
Bibliographic record
Abstract
Water movement affects carbon and nitrogen isotopic signatures of algae, possibly through effects on boundarylayer thickness. We describe a laboratory experiment carried out in artificial streams, which supports this hypothesis. Periphyton δ13C and δ15N signatures were significantly depleted in 13C and 15N when grown at higher water velocity over the range of 5–62 cm s−1, the normal range found in small streams. δ13C signatures ranged between —16.7‰ and —28.1‰ from the lowest to the highest water velocity, respectively. Similarly, δ15N signatures ranged between 7.2‰ and 2.3‰ from the lowest to the highest water velocity, respectively. This pattern was found for algal communities growing on glass (mainly diatoms) as well as for those growing on rock (mainly filamentous green algae). For both C and N, the slopes of the responses were not different between the periphyton communities growing on each substrate, although the effect was statistically weaker for the communities on rocks. The intercept for δ13C was significantly higher for the communities on rocks, but not different for δ15N. Thus, while both isotopes are fractionated to a greater extent as velocity increases, the diatom communities growing on glass appeared to fractionate C isotopes more than the filamentous green algae growing on rock. This relationship between the stable isotopic signatures of aquatic plants and water velocity will hopefully allow a better understanding of the differences in isotopic signatures of fish in different habitats.
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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 | 0.001 |
| 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 | 0.000 | 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".