Revisiting carbon cycling in the Laurentian Great Lakes following dreissenid mussel invasion
Bibliographic record
Abstract
Abstract Since the active role of inland waters in cycling carbon (C) has been revealed, there has been a renewed interest in calculating C budgets for inland waters to understand their role with respect to global climate change. There is a lack of knowledge regarding C cycling in the Laurentian Great Lakes, the worlds largest freshwater reservoir, with current estimates neglecting the role of invasive species. For one of the most pervasive invaders, dreissenid (zebra and quagga) mussels, research has focused on filter feeding impacts on phosphorus dynamics, but there is a lack of knowledge regarding their role in C cycling, specifically, the impact of the C stored in their slowly degrading shells. As such, we set out to estimate the mass of empty shells and C stored in those shells. We calculated an estimated 1.19 E10 tonnes of empty shell mass currently sitting at the bottom of these lakes, which store approximately 1.43 E9 tonnes of C. This scale of inorganic C storage is comparable to rates of organic C storage in nature-based climate solutions. This work demonstrates the importance of a previously unexplored pathway that dreissenid mussels are altering C cycling in the Laurentian Great Lakes and the thousands of other invaded lakes and rivers.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".