Typhoons initiate predictable change in aquatic bacterial communities
Bibliographic record
Abstract
We explored patterns of change in bacterioplankton and phytoplankton community composition in response to typhoons in a subtropical, subalpine freshwater humic lake in Taiwan. Thermal profiles with depth, nearsurface dissolved oxygen, and meteorological conditions were measured at high temporal resolution using an instrumented buoy. We collected samples across a time series spanning two typhoon seasons and assessed bacterial community composition at each time point using a fingerprinting technique targeting the internal transcribed spacer region of the ribosomal RNA operon. Wind and rain associated with the typhoons mixed the otherwise stably stratified water column. After each of six typhoon‐induced mixing events, the bacterial community composition was reset to similar conditions, revealing a surprisingly deterministic pattern of recovery that was reminiscent of secondary succession following large infrequent disturbances in terrestrial ecosystems. Distinct bacterial assemblages were associated with epilimnion and hypolimnion samples collected more than 80 d after a mixing event. These assemblages were also distinct from the communities observed in the generally unstratified water column within 7 d after a typhoon. Our results were surprising since bacterial communities are not expected to respond in such predictable ways because of their immense complexity and diversity. In contrast, phytoplankton communities did not recover in a predictable way after typhoons.
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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 | 0.000 |
| 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".