Poor direct exploitation of terrestrial particulate organic material from peat layers by <i>Daphnia galeata</i>
Bibliographic record
Abstract
Terrestrial organic material (t-OM) can subsidize lake food webs indirectly via incorporation of dissolved t-OM by bacteria and subsequent transfer to higher trophic levels or directly through metazoan consumption of particulate t-OM (t-POM). We tested the effects of peat layer t-POM on Daphnia galeata performance. A pure t-POM diet could not sustain survival, growth, and reproduction of D. galeata. Mixtures of heterotrophic bacteria (Pseudomonas sp.) and phytoplankton (Rhodomonas lacustris) gave higher survival, growth, and reproduction than mixtures of t-POM and Rhodomonas. Daphnids performed best when feeding on pure Rhodomonas diets. Quantification of phosphorus (P) and essential biochemicals (i.e., fatty acids) revealed that Rhodomonas had the highest amounts of all these components. Pseudomonas, while rich in P, contained few essential fatty acids, and t-POM had low concentrations of both P and fatty acids. We therefore suggest that the poor food quality of t-POM in our experiment was due to its suboptimal mineral and biochemical composition and that a substantial proportion of high-quality phytoplankton is necessary to sustain zooplankton biomass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".