Sources of variability and comparability between salmonid stomach contents and isotopic analyses: study design lessons and recommendations
Bibliographic record
Abstract
We compared sources of variability and cost in paired stomach content and stable isotope samples from three salmonid species collected in September 2001–2005 and describe the relative information provided by each method in terms of measuring diet overlap and food web study design. Based on diet analyses, diet overlap among brown trout, rainbow trout, and mountain whitefish was high, and we observed little variation in diets among years. In contrast, for sample sizes n ≥ 25, 95% confidence interval (CI) around mean δ 15 Ν and δ 13 C for the three target species did not overlap, and species, year, and fish size effects were significantly different, implying that these species likely consumed similar prey but in different proportions. Stable isotope processing costs were US$12 per sample, while stomach content analysis costs averaged US$25.49 ± $2.91 (95% CI) and ranged from US$1.50 for an empty stomach to US$291.50 for a sample with 2330 items. Precision in both δ 15 Ν and δ 13 C and mean diet overlap values based on stomach contents increased considerably up to a sample size of n = 10 and plateaued around n = 25, with little further increase in precision.
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 | 0.437 | 0.520 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.016 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".