Are there hot spots for <i>Elliptio complanata</i> in the shallow littoral zone of a large Canadian Shield lake?
Bibliographic record
Abstract
The variability in habitat conditions in large lake basins may provide areas favourable for unionid recruitment and growth. Here we test whether the distribution, body size, and growth rate of Elliptio complanata differ between upwind and downwind sites in a 5.8 km long lake basin. We measured density and morphological characteristics of E. complanata at nine upwind and nine downwind sites. We also measured growth rates and stable isotope (δ13C, δ15N) signatures at three upwind and three downwind sites. We found no difference in density or body size of E. complanata between upwind and downwind sites, despite clear differences in water temperature and food availability. Contrary to our expectations, the growth rate of E. complanata was higher (by 62%) at upwind sites. The reason for this difference is uncertain, but higher shell erosion and a negative relationship between δ13C and C/N ratio suggest that E. complanata may be more stressed at downwind sites. The δ15N of E. complanata did not differ systematically between upwind and downwind sites, but increased with increasing body size. It would therefore be prudent to standardize the size of E. complanata used to measure the baseline of lake food webs.
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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 | 0.002 | 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".