Checking the pulse of Lake Ontario's microbial-planktonic communities: A trophic transfer hypothesis
Bibliographic record
Abstract
The structure and function of the microbial food web of Lake Ontario was assessed at 15 stations distributed across 4 transects during the spring and summer of 2003. This was the first major binational study of Lake Ontario since the Lake Ontario Trophic Transfer initiative of 1990. The microbial loop (bacteria, autotrophic picoplankton, heterotrophic nanoflagellates (HNF) and ciliates) and phytoplankton, were enumerated microscopically in addition to measurements of chlorophyll a, size fractionated primary productivity (14C) and bacterial growth (3H). HNF dominated the total biomass in spring (≈300 mg m−3) and summer (≈1250 mg m−3). The size of the organic carbon pool increased from ≈90 mg C m−3 in spring to ≈270 mg C m−3 with HNF contributing 36% of the total organic carbon in the spring and 52% in the summer; however the net balance of the organic carbon pool shifted from autotrophic in the spring to heterotrophic in the summer. The available evidence suggests that HNF are a poor quality food resource for zooplankton and it is likely that the carbon sequestered by HNF is not available to higher trophic levels resulting in dietary stress for planktivores. The implications of high HNF for both organic carbon cycling and maintaining healthy fisheries needs further research. Independent observations show that oligotrophic conditions prevail as evidenced by low phosphorus, low chlorophyll a, low plankton and high water clarity. Such conditions have been generally regarded as the gold standard for managing healthy lakes. Lake Ontario is oligotrophic and healthy from a water quality perspective, but from a food web dynamics point of view, Lake Ontario appears to be unhealthy due to the dominance of HNF, low zooplankton and poor quality of food available to higher trophic levels. We hypothesize that the lake's poor health is attributable to inefficient energy transfer from lower to higher trophic levels. The traditional understanding of trophic state based mainly on water quality criteria needs to be broadened by the inclusion of food web and fisheries based metrics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".