Efficiencies of benthic and pelagic trophic pathways in a subalpine lake
Bibliographic record
Abstract
Although the study of lakes has traditionally focused on pelagic production pathways, recent stable isotope and diet evidence indicates that benthic algal production is an important contributor to fish production. This has led to the suggestion that energy may be more efficiently passed along benthic food chains relative to their pelagic counterparts. To test this idea, we combined stable isotope based assessments of energy flow pathways with estimates of pelagic- and benthic-based primary and secondary production in Castle Lake, California. Approximately 50% of whole-lake primary production and 30% of whole-lake secondary production occurred in benthic habitats. Stable carbon isotopes and dietary data indicated that fish were predominantly supported by benthic (63%) and terrestrial (24%) secondary production. Ecological efficiencies (algal production / invertebrate production) were low in Castle Lake (<3%), though zoobenthic production was more efficiently passed to fish than was zooplankton production. The larger size of benthic prey relative to pelagic prey may affect fish prey selection and foraging efficiency, resulting in differences in ecological efficiency between pelagic and benthic trophic pathways.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".