Source attribution of agriculture-related deposition by using total nitrogen and δ<sup>15</sup>N in epiphytic lichen tissue, bark and deposition water samples in Germany
Bibliographic record
Abstract
Compared with physico-chemical deposition measurement methods, lichens are able to identify the long-term overall effects of high N pollution concentrations in the air. In addition, the natural abundances of the stable isotope of N, (15)N, are being widely used in research on N cycling in ecosystems. They can also be used as instruments for source attribution. In this study, epiphytic lichens were tested to determine whether their respective N content and δ(15)N ratios can be used to estimate N deposition rates and to locate various sources of N compounds. Epiphytic lichen and bark samples were collected from around various deposition measurement field stations at different sites in the western part of Germany. The N content of epiphytic lichens reflects the species-specific, agriculture-related circumstances of N deposition at various sites in Germany. At the same time, δ(15)N signatures of the different investigated epiphytic lichen species and bark samples are highly depleted in (15)N under high ammonium deposition. The different surface types of lichens and barks exhibit different concentrations of N and δ(15)N ratios, despite being exposed to similar N deposition rates. The verification of highly negative δ(15)N ratios at sites with local and regional emitters shows that source attribution is possible by comparing different δ(15)N signatures in areas with a wide range of different N deposition types and the corresponding differences in δ(15)N among various source N pools. Especially nitrophytic lichens can support the on-site instrumentation measuring N deposition by qualification and quantification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".