The effects of preservation on fish tissue stable isotope signatures
Bibliographic record
Abstract
The effects of formalin and ethanol preservation on the δ13C and δ15N isotope signatures of Arctic charr Salvelinus alpinus muscle tissue were examined. The lipid content of the tissue samples studied ranged from 3·6 to 6·1% and was not correlated with the magnitude of observed isotopic shifts in preserved samples. Ethanol and formalin significantly depleted and enriched, respectively, the δ13C isotope signatures of preserved tissues when compared to control samples. Ethanol did not significantly enrich δ15N signatures in comparison to controls, whereas formalin did. A meta‐analysis of multiple species effects further demonstrated significant preservation effects in fish tissue. Statistical analysis of data obtained by correcting preserved tissue isotope signatures with literature, bootstrapped or meta‐analysis derived correction factors demonstrated significant differences between corrected and control sample isotope signatures or failure to produce a unity slope when the data sets were regressed against one another. Species‐specific, bootstrapped linear correction models resulted in no such errors. Results suggest that species‐specific correction methods should be used for fishes because of the known wide variation in fish tissue lipid content and composition. Accordingly, the use of pilot studies will be required to develop correction factors that properly adjust for preservation effects when interpreting temporal patterns in historic analyses of food webs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".