The importance of microzooplankton versus phytoplankton to copepod populations during late winter and early spring in Lake Michigan
Bibliographic record
Abstract
Feeding rates of the calanoid copepod Leptodiaptomus sicilis on natural assemblages of phytoplankton and microzooplankton were evaluated during late winter and early spring in Lake Michigan. Microzooplankton were the preferred food source for this copepod, and larger size fractions of phytoplankton were preferred to smaller size fractions. Ingestion rates of total chlorophyll a ranged from 2 to 14 ng·copepod1·day1, while ingestion rates of micro zoo plank ton biomass ranged from 0.04 to 0.15 µg C·copepod1·day1. In these experiments, microzooplankton carbon accounted for 22%74% of the total carbon ingested. Clearance rates of microzooplankton carbon were positively related to the larger size fractions of chlorophyll a and to total suspended solids. Measured ingestion rates of microzooplankton and phyto plankton carbon suggest that calanoid copepod populations have the potential to control microzooplankton production in late winter and early spring, and even with an abundance of phytoplankton carbon, food availability may limit the reproduction of L. sicilis. Because microzooplankton contribute significantly to the diet of these copepods, stimulation of the microbial food web by terrigenous inputs of nutrients and carbon may be transmitted to higher trophic levels (i.e., mesozooplankton and their predators) through heterotrophic flagellates and protozoans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".