A global estimator of C and N isotope baselines for fresh waters
Bibliographic record
Abstract
Abstract Baselines are the pebbles in the shoes of isotope ecologists. The extreme variability of the isotope composition of resources at the base of food webs governs the spatial differences of consumers' isotope composition, so that isotope‐inferred trophic properties can be compared across ecosystems only after correction for baseline effects. However, acquiring comparable and reproducible isotopic baselines in different lakes and rivers has been so challenging that many isotope datasets lack baseline values. Global estimates of C and N isotopic baselines would considerably expand the scope of large‐scale isotope analyses in ecology. Cross‐referencing the global freshwater isotope database ISOFRESH (>800 sites across five continents) with a set of environmental attributes describing hydrology, physiography, climate, land use, soils and anthropogenic influences, we built data‐driven models that predict C and N stable isotope compositions of benthic and pelagic/open‐water baseline organisms for lakes and rivers with an error within 11%–13% of the overall range of values observed worldwide. We then applied the models globally to predict spatial patterns in isotope baselines. We showed simulated baselines accurately preserve patterns in across‐site variability for most of the common isotope‐derived trophic metrics computed for freshwater fish at the population and community levels. We conclude with guidance on the best use of such predictive baseline models, highlighting their usefulness for filling in gaps in meta‐ecological analyses that test regional or global drivers of food web structure, but caution against substituting them for measured values in local‐scale studies.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".