Bibliographic record
Abstract
Autotrophic picoplankton (APP) are distributed worldwide and are ubiquitous in all types of lakes of varying trophic state. APP are major players in carbon production in all aquatic ecosystems, including extreme environments such as cold ice-covered and/or warm tropical lakes and thermal springs. They often form the base of complex microbial food webs, becoming prey for a multitude of protozoan and micro-invertebrate grazers, that effectively channel APP carbon to higher trophic levels including fish. In this review we examine the existing literature on freshwater autotrophic picoplankton, setting recent findings and current ecological issues within an historic framework, and include a description of the occurrence and distribution of both single-cell and colonial APP (picocyanobacteria) in different types of lakes. In this review we place considerable emphasis on methodology and ecology, including sampling, counting, preservation, molecular techniques, measurement of photosynthesis, and include extensive comment on their important role in microbial food webs. The model outlined by Stockner of an increase of APP abundance and biomass and a decrease of its relative importance with the increase of phosphorus concentration in lakes has been widely accepted, and only recently confirmed in marine and freshwater ecosystems. Nevertheless the relationship which drives the APP presence and importance in lakes of differing trophic status appears with considerable variation so we must conclude that the success of APP in oligotrophic lakes worldwide is not a certainty but highly probable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".