Microbial foodweb comparison of the Laurentian Great Lakes during the summers of 2001–2004
Bibliographic record
Abstract
A structural and functional assessment of the microbial foodweb of Lakes Superior, Huron, Erie and Ontario was undertaken during late summer (August) between 2001 and 2004. One lake was sampled in each year. Our analysis included microscopic enumerations of bacteria, autotrophic picoplankton, phytoplankton, heterotrophic nanoflagellates and ciliates coupled with radioisotope tracer measurements of primary productivity (14C) and bacterial growth (3H). Phytoplankton biomass was highest on average in Lake Erie (≈1.9 g m−3) and lowest in Lake Ontario (≈0.2 g m−3), whereas microbial loop biomass was highest in Lake Ontario (≈2.4 g m−3) and lowest in Lake Huron (≈0.1 g m−3). The organic carbon pool was found to be predominantly autotrophic (>80%) in Lakes Superior (≈280 mg C m−3), Huron (≈195 mg C m−3) and Erie (≈660 mg C m−3) and smaller picoplankton had the highest carbon turnover rates (≈0.4–1.5 d−1). However, in Lake Ontario (≈335 mg C m−3) the carbon pool was about 75% heterotrophic and larger net plankton had the highest carbon turnover rates (≈6.8 d−1). Despite differences in trophic state, the microbial foodwebs of Lakes Superior, Huron and Erie appeared to function in a similar and efficient manner. In contrast, the microbial foodweb of Lake Ontario appeared to be unhealthy with autochthonous production being sequestered by heterotrophic nanoflagellates. More detailed work is needed to understand the foodweb linkages both within the Great Lakes and among them.
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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 | 0.000 |
| 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".