Rates and components of carbon turnover in fish muscle: insights from bioenergetics models and a whole-lake <sup>13</sup>C addition
Bibliographic record
Abstract
Stable isotopes are widely employed to describe energy flow in aquatic communities, though interpretation of results can be confounded by the fact that organisms integrate over vastly different time scales. We used results from a 56-day whole-lake 13C addition and a bioenergetic modeling approach to estimate dorsal muscle carbon turnover rates in a natural setting for three sizes of bluegill ( Lepomis macrochirus ), largemouth bass ( Micropterus salmoides ), and yellow perch ( Perca flavescens ). Generally, dynamic δ13C models with a metabolic tissue replacement term were better supported than models predicting isotopic change from growth alone, except when relative growth rates were highest (age 0 bluegill). Across species and size classes, the percentage of carbon change due to tissue replacement was variable (2%–80%) and independent of fish size. The half-life of δ13C in age 0 fishes was similar and ranged from 8 to 18 days. In contrast, adult tissue half-lives were much longer (116–173 days). Based on these and previously published estimates, fish mass (g) was a strong predictor of fish carbon turnover rates, λ: log(λ) = –3.65 – 0.20 log(mass), r2 = 0.71.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".