Grazing preferences of three species of amoebae on cyanobacteria and green algae
Bibliographic record
Abstract
Twenty species/isolates of cyanobacteria and green algae were isolated from cyanobacterial bloom samples in lakes associated with the upper Qu'Appelle River drainage system in southern Saskatchewan, Canada. Three amoebae species (Cochliopodium sp., Vannella sp. and Vermamoeba vermiformis) were also isolated from one of these samples, and were subjected to grazing assays to determine which species of cyanobacteria or algae could potentially serve as a food source. Amoeba grazing rates were quantified based on the diameter of the plaque after 12 days on agar plate assays, and by estimation of the amoeba population growth rate from the rate of increase of plaque area. The common cyanobacterial bloom-formers Dolichospermum sp. and Aphanizomenon flos-aquae supported high growth rates for all three amoebae, while green algae, with the exception of one green alga/amoeba combination, did not support growth of the tested amoebae. Many of the cyanobacterial and algal isolates that did not support amoebae growth were ingested, suggesting that ingestion did not determine grazing success. Overall, while the cyanobacteria Dolichospermum sp. and Aphanizomenon flos-aquae were suitable food sources for the amoebae, the other cyanobacteria were grazed in an unpredictable manner, with some species/strains grazed by some amoebae and some species not grazed at all.
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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 | 0.001 |
| 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".