Foodweb analysis of the Orinoco floodplain based on production estimates and stable isotope data
Bibliographic record
Abstract
Stable isotope data and indirect methods for estimating consumer production were used in a foodweb analysis for the Orinoco River floodplain, Venezuela. The ratio of annual production to mean annual biomass (Pa/B̄) was estimated from mass at maturity for all major categories of consumers. Field data on B̄ then were used to estimate Pa for each major category. Carbon sources for all categories of consumers were identified through δ13C analysis, and trophic shifts in δ15N were used in assigning trophic levels to consumers. The ultimate C source for both invertebrates and fish was algae (phytoplankton and periphyton), even though macrophytes and litterfall from the floodplain forest composed 98% of potentially available C. Production of invertebrate consumers (benthos, rhizofauna, and zooplankton), which occurred almost entirely through 1st-level consumption, was estimated as 14 g C m−2 y−1. Fish, which produced 11 g C m−2 y−1, showed a mean δ15N trophic shift of 1.8 units above the algal C source, suggesting that ∼20% of fish production was directly accountable to consumption of algal C (1st-level consumption), and that most of the remainder was attributable to 1st-level carnivory. Data on production and trophic shifts implied trophic efficiency of 5% for invertebrates and 20% for fish. Although the food web is focused on a very small fraction of potentially available primary C (algae), this C source can account quantitatively for the observed production of both invertebrates and fish. The food web showed marked trophic compression (large consumers supported by trophic levels 1 and 2), which is the only means by which high production of large consumers can be sustained on a small fraction of the potentially available C.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".