Fish decomposition in boreal lakes and biogeochemical implications
Bibliographic record
Abstract
A field study in a boreal lake using a remotely operated vehicle equipped with a camera established that falling fish carcasses did not tend to be buried in sediments after deposition. Decomposition rates of fish carcasses in three boreal lakes were experimentally assessed at different depths. In shallow waters (between 0 and 4 m), decomposition was fast (half lives, τ, ranging from 40 to 230 h) and controlled by vertebrates. In deep waters (below the thermocline), decomposition was slow (τ between 770 and 1,733 h) and was controlled by bacterial processes. Water temperature was a promising predictor of decomposition half‐lives in freshwater. Using a novel underwater infrared camera system, we identified the daily and seasonal patterns of scavenging activity by littoral fish. Only three species displayed scavenging behavior, with creek chubs being the most active. Fast fish‐mediated littoral recycling of fish carcass may explain the lack of direct observations of carcasses in lakes. Estimates of phosphorus fluxes in one of the studied lakes indicate that falling carcasses can represent a significant water‐to‐sediment flux of nutrient.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".