Alternative states drive the patterns in the bacterioplankton composition in shallow <scp>P</scp> ampean lakes ( <scp>A</scp> rgentina)
Bibliographic record
Abstract
We assessed the influence of environmental factors in shaping the free-living bacterial community structure in a set of shallow lakes characterized by contrasting stable state patterns (clear-vegetated, inorganic-turbid and phytoplankton-turbid). Six temperate shallow lakes from the Pampa Plain (Argentina) were sampled over an annual cycle, and two fingerprinting techniques were applied: a 16S rDNA analysis was performed using denaturing gradient gel electrophoresis (DGGE) profiles, and a 16S-23S internally transcribed spacer region analysis was conducted by means of automated ribosomal intergenic spacer analysis (ARISA) profiles. Our results show that the steady state that characterized the different shallow lakes played a major role in structuring the community: the composition of free-living bacteria differed significantly between clear-vegetated, inorganic-turbid and phytoplankton-turbid shallow lakes. The state of the system was more important in determining these patterns than seasonality, geographical location or degree of hydrological connectivity. Moreover, this strong environmental control was particularly evident in the pattern observed in one of the lakes, which shifted from a clear to a turbid state over the course of the study. This lake showed a directional selection of species from a typical clear-like to a turbid-like community. The combined DGGE/ARISA approach revealed not only broad patterns among different alternative steady states, but also more subtle differences within different regimes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".