Freshwater pearl mussels as a stream water stable isotope recorder
Bibliographic record
Abstract
Abstract For several decades, stable isotopes have been a commonly used and effective tool for flow path analysis, stream water source apportionment, and transit time analysis. The Global Network of Isotopes in Precipitation repository now has monthly precipitation isotope time series extending over several years and even decades in some settings. However, stream water isotope composition time series remain rather short with only very few data sets spanning over more than a few years. A critical challenge in this respect is the collection of stream water isotope data sets across a wide variety of headwater streams and for long durations. We rely on a new approach for stream signal reconstruction based on freshwater mussels, specifically the freshwater pearl mussel Margaritifera margaritifera . We use secondary ion mass spectrometry (SIMS) to quantify oxygen isotope ratios in pearl mussel shell growth bands. In our study area, the observed seasonal variability in precipitation δ 18 O values ranges between −15‰ and −3‰. This input signal is strongly damped in stream water, where observed values of δ 18 O range between −10‰ and −6.5‰. These values are consistent with our measured average shell‐derived stream water δ 18 O of −7.19‰. Along successive growth bands, SIMS‐based stream water δ 18 O w values varied within a seasonal range of −9‰ to −5‰. The proposed SIMS‐based shell analysis technique is obviously well suited for analysing isotopic signatures of O in shell material—especially from the perspective of reconstructing historical series of in‐stream isotope signatures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.486 | 0.178 |
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; both teacher heads 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".