Bibliographic record
Abstract
Summary The goal of this lecture is to provide aquatic scientists and interested laymen with an overview of the potential, methods and exemplary analyses of DNA preserved in lake sediments. This area is an emerging field, as new techniques are opening up avenues for novel studies of the sediment record. Numerous papers in this field have recently been published in Science, PNAS and PLoS ONE. Like many new fields, there are challenges as well as exciting lines of future inquiry, which we dedicate part of the lecture towards. This lecture starts by providing a brief introduction to paleolimnology, with an emphasis on how DNA studies can expand this field. We then provide information on the ways in which DNA can be archived in sediments & how analyses can differ, depending on the question and target. Examples of the common genetic markers used and how DNA may be sequenced are also highlighted in the methods section. In the second portion of the lecture, we focus on the applications of sedimentary DNA: 1) to the study of particular phytoplankton group dynamics; 2) to the analysis of zooplankton DNA preserved in resting eggs; and, 3) to uncover community‐wide changes in plankton. Finally, we close the lecture with a discussion on challenges and future directions in the field. Advances in the development and calibration of different extraction techniques, as well as further enhancement of genetic libraries and bioinformatics pipelines, are all areas ripe for new research. This lecture has been prepared with a diverse audience in mind. For example, undergraduate or graduate courses that could be interested in our material include Aquatic Ecology, Limnology, Oceanography, Microbial Ecology, Environmental Genomics and Paleoecology. This lecture could also serve as a useful introduction to non‐specialist audiences that are interested in the potential of the DNA archive preserved in lake sediments (including funding agencies).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".