Bridging between litterbags and whole-ecosystem experiments: a new approach for studying lake sediments
Why this work is in the frame
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Bibliographic record
Abstract
<p>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.<br /><br /><img src="/public/site/images/ttaccini/88x31.png" alt="" /> <br />This work is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">Creative Commons Attribution 4.0 International</a>.</p>
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 it