Modeling the influence of salmon spawning on hyporheic exchange of marine-derived nutrients in gravel stream beds
Bibliographic record
Abstract
Salmon that spawn in streams deliver marine-derived nutrients (MDN) that catalyze trophic productivity and support rearing juvenile salmon. Salmon spawning also affects hyporheic exchange and movement of dissolved MDN through the stream bed by creating redd topography that induces pumping exchange and by winnowing fine sediment and loosening the bed, which alters hydraulic conductivity and bed porosity. The spatial extent of spawning within the channel likely governs the volume and rate of dissolved MDN exchanged with the stream bed through this process. To explore this issue, we used a two-dimensional groundwater model to predict changes in hyporheic volume, flux, and mean hydraulic residence time of dissolved MDN as a function of the proportion of the bed surface occupied by redds (P). Predictions indicate that hyporheic volume and flux systematically increase with P, while the mean hydraulic residence time of dissolved MDN in the hyporheic zone decreases sharply with P, from 5.79 h on an unspawned bed (P = 0) to 0.03 h for a mass-spawned bed (P = 1.0). Shorter residence time results from hyporheic flux increasing faster than hyporheic volume with higher P. Implications for uptake of dissolved MDN are explored with Damköhler numbers, defined as the ratio of the mean hydraulic residence time to a biogeochemical rate of interest. Given the considerable influence of spawning on hyporheic exchange, additional research is needed to determine conditions under which bioassimilation of dissolved MDN is limited by nutrient supply, extent of the hyporheic zone, or processing rate of MDN in stream beds.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".