Analysis of plankton size spectra irregularities in two subtropical shallow lakes (Esteros del Iberá, Argentina)
Bibliographic record
Abstract
Biomass irregularities in the plankton size spectra of two subtropical shallow lakes have been quantified assuming the classical observed generalities of the size spectra. During a seasonal cycle, three main functional size ranges determined the allocation of the spectra irregularities: microbial food web, nanoplanktonmicroplankton autotrophs, and herbivorous organisms. The structural adjustments within these trophic positions responded to the internal competition between functional guilds, mainly as the result of size-based characteristics related to the ability to eat and the susceptibility to be eaten. Despite the existence of a typical spectrum undulation resulting from self-organization (well-defined trophic positions, limneticbenthic interaction), the biomass irregularities were an indicator of the main interactions disturbing the steady state. The mechanisms responsible for the irregularities operated jointly at ecosystem and individual levels. Thus, the irregular spectra of the eutrophic Laguna Iberá suggested a strong top-down control through cascade effects. Specific properties of peculiar organisms like filamentous cyanobacteria contributed to hold these stable irregularities. The higher spectrum regularity of the meso-oligotrophic Laguna Galarza emerged from a more balanced flow of biomass along the food chain.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".