Gross primary production and respiration differences among littoral and pelagic habitats in northern Wisconsin lakes
Bibliographic record
Abstract
Net ecosystem production (NEP) trends among lakes have been ascribed to differences in nutrient and allochthonous carbon inputs, but little is known on how different habitats within lakes contribute to these trends. We sampled pelagic and littoral surface waters using sonde (i.e., free-water) and bottle methods concurrently in lakes spanning a range of trophic conditions. We considered whether the typically higher metabolism estimates found with sonde methods are due to contributions from littoral habitats not reflected by bottle estimates. We sought the source of littoral contributions by selecting sites with maximum differences in macrophyte abundance. Sonde estimates for pelagic primary production and respiration were twothree times greater than bottle estimates. Sonde/bottle ratios were higher in productive lakes and lakes with more littoral area. Bottle estimates were similar among all sites, and sonde estimates in macrophyte-poor sites were similar to pelagic sondes. However, sonde estimates in macrophyte-rich areas were fournine times greater than bottle estimates. Results suggest littoral zones increase whole-lake NEP in eutrophic systems, whereas the Sphagnum mat surrounding dystrophic lakes decreases NEP. Non-planktonic organisms associated with macrophytes provide important littoral contributions to whole-lake metabolism and to understanding NEP trends among lakes.
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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 | 0.000 |
| 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".