Physical forces constrain the depth distribution of the abundant native mussel <i>Elliptio complanata</i> in lakes
Bibliographic record
Abstract
Summary 1. Unionid mussels often account for a large portion of benthic biomass and contribute to nutrient cycling and sediment processes, but are thought to be limited to shallow areas (<2–3 m). 2. The depth distribution and body size of Elliptio complanata were compared in seven Canadian Shield lake basins of different sizes to test what factors determine the upper and lower limit of their depth range. Specifically, I tested whether (i) the upper range of their distribution is limited by exposure to winds and wave action and (ii) the lower range of their distribution is limited by the depth of the thermocline or by the boundary of mud deposition. 3. The average depth distribution of E. complanata shifted to greater depths in larger lake basins. When comparing individual transects, maximum mussel density was found deeper at more exposed sites. Mussel size decreased with increasing depth and was larger, on average, in larger lake basins. These results suggest that physical forces limit the upper range of mussel distribution in lakes. 4. The maximum depth at which mussels were found in different lakes was closely related to thermocline depth. However, mussels were commonly observed below the predicted depth of the mud deposition boundary. The thermocline limits the lower range of mussel distribution in lakes, probably by limiting food availability and by determining water temperature. Substratum type does not limit the lower distribution of mussels. 5. These results suggest that unionid mussels are present in the deeper parts of the littoral zone, especially in large lakes. Therefore, comparisons of mussel populations between sites and between lakes would be biased unless the full depth distribution of these mussels is considered. These results also suggest that long‐term changes in the thermal structure of lakes could affect the range of unionid mussel populations and their functional role in littoral ecosystems.
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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 | 0.000 | 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 | 0.001 |
| 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".