An assessment of chlorophyll preservation in lake sediments using multiple analytical techniques applied to the annually laminated lake sediments of Nylandssjön
Bibliographic record
Abstract
Abstract Chlorophyll is frequently used as a proxy for autochthonous production in lakes. This use of chlorophyll concentrations in sediments to infer historical changes in lake primary production relies heavily on the assumption that preservation is sufficient to reflect the productivity in a meaningful way. In this study, we use a series of freeze cores from a lake with annually laminated sediments to assess how reliable down-core trends in chlorophyll are, and to what extent chlorophyll is degraded in the sediment. A striking consistency in the down-core chlorophyll trends in four sediment cores collected in different years (1983, 1992, 2002 and 2010) shows that the sediment preserves a consistent chlorophyll signal over longer timescales. However, there are also clear signs that diagenetic processes within the sediment affect the chlorophyll concentration in sediment layers younger than 10–15 years. This implies that care is needed when interpreting chlorophyll trends in recent sediments. Further, our data show that high-performance liquid chromatography (HPLC) and visible reflectance spectroscopy (VRS) detect similar chlorophyll concentrations for recently dried samples. A third analytical technique, pyrolysis–gas chromatography/mass spectrometry, which provides semi-quantitative values for chlorophyll, also produce a temporal trend that is highly correlated with data from the two quantitative techniques. For samples that have been stored dried at room temperature for several years there is, however, a large discrepancy between the two quantitative techniques. The VRS method is more robust with regards to degradation during storage, while HPLC results demonstrate clear storage effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".