Bridging between litterbags and whole-ecosystem experiments: a new approach for studying lake sediments
Bibliographic record
Abstract
Nearshore sediments have a major influence over the functioning of aquatic ecosystems, but predicting their response to future environmental change has proven difficult. Previous manipulative experiments have faced challenges controlling environmental conditions, replicating sediment mixing dynamics, and extrapolating across spatial scales. Here we describe a new approach to manipulate lake sediments that overcomes previous concerns about reproducibility and environment controls, whilst also bridging the gap between smaller microcosm or litterbag experiments and whole-ecosystem manipulations. Our approach involves submerging moderate-sized (~15 L) artificial substrates that have been standardised to mimic natural sediments within the littoral zones of lakes. We show that this approach can accurately mirror the absolute dissolved organic carbon concentrations and pH of pore water, and to a lesser degree inorganic carbon concentrations, from natural lake sediments with similar organic matter profiles. On a relative basis, all measured variables had similar temporal dynamics between artificial and adjacent natural sediments. Late-summer zooplankton biomass also did not differ between natural and artificial sediments. By offering a more realistic way to manipulate freshwater sediments than previously possible, our approach can improve predictions of lake ecosystems in a changing world. This work is licensed under a Creative Commons Attribution 4.0 International.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".