Algal-silica cycling and pigment diagenesis in recent alpine lake sediments: mechanisms and paleoecological
Bibliographic record
Abstract
The quality and interpretability of the paleobiological record depends on the preservation of morphological and geochemical fossils. Siliceous microfossils and sedimentary pigments are often cor- nerstones in paleoecology, although the microbial and geochemical processes conducive to their preservation remain poorly constrained. We examined sediments from an alpine lake in Banff National Park (Alberta, Canada) where diatom frustules are completely dis- solved within 50 years of deposition. Diatom dissolu- tion, silica recycling, and diagenetic alteration of algal pigments were investigated, in conjunction with pore- water geochemistry and microelectrode profiling of the sediment-water interface. Analysis of sediment trap material showed *90% of biogenic silica (BSi) production is lost prior to burial. Silica flux calculations, based on dissolved silica (as H4SiO4) in pore-waters, show a further*6% of total BSi is returned to the water column from the upper 4 cm of sediments, implying that only *4% of total BSi is permanently archived in sediments. In situ sediment pH and O2 profiles reveal that aerobic respiration by bacteria fully consumes oxygen by a depth of 4 mm into the sediment, with associated strong pH and redox gradients. During sedimentation and early diagenesis, diatoms undergo loss of extracellular polymeric substances that coat their frustules, promoting silica dissolution and leading to the loss of the microfossil record by a depth of 3.25 cm. Sedimentary pigments similarly undergo rapid degra- dation, but diatom-related carotenoids persist below the depth of silica dissolution. This work provides new insights on diagenetic processes in lakes, with broad implications for the interpretation of sedimentary prox- ies for algal production.
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 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 | 0.001 |
| 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) | 0.000 | 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 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".