Aquatic and Terrestrial Plant Contributions to Sedimentary Plant Waxes in a Modern Arctic Lake Setting
Bibliographic record
Abstract
Abstract Sedimentary plant waxδ2H values are common proxies for hydrology, a poorly constrained variable in the Arctic. However, it can be difficult to distinguish plant waxes derived from aquatic versus terrestrial plants, causing uncertainty in climate interpretations. We test the hypothesis that Arctic lake sediment mid‐ and long‐chain plant waxes derive from aquatic and terrestrial plants, respectively. We comparen‐alkanoic acid andn‐alkane chain‐length distributions andn‐alkanoic acidδ2H andδ13C values of the 29 most abundant modern plant taxa to those for soils, water filtrates, and lake sediments in the Qaupat Lake (QPT) catchment, Nunavut, Canada. Chain length distributions are variable among terrestrial plants, but similar and dominated by mid‐chain waxes among submerged/floating aquatic plants. Sedimentary wax distributions are similar to those in submerged/floating aquatic plants and toSalixspp., which are among the most abundant terrestrial plants in the QPT catchment. Mid‐chainn‐alkanoic acidδ2H values are similar in sediments and submerged/floating aquatic plants, but 50‰ lower thanSalixspp. In contrast, sedimentary long‐chainn‐alkanoic acidδ2H values fall between those for submerged/floating aquatic plants andSalixspp. We therefore infer that mid‐chain waxes in QPT are primarily from aquatic plants, whereas long‐chain waxes are from a mix of terrestrial and aquatic plants. In Arctic lakes like QPT, terrestrial wax transport via leaf litter and surface flow is limited by low‐lying topography and sparse vegetation. If these lakes also have abundant aquatic plants growing near the sediment‐water interface, the aquatic plants can contribute large portions of sedimentary waxes.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".