A protocol for the analysis of aquatic biodiversity by multiple β diversities
Bibliographic record
Abstract
Species assemblage can be fairly unstable in aquatic ecosystems because of strong environmental constraints. Measuring β diversity is one of the most useful tools for assessing community dynamics and differentiations among communities. Whereas many β diversity measurements have been proposed for these issues, we present here a unifying framework based on the properties of Hill number, q, to unravel the complex changes occurring in dynamic communities. Using ideal numerical examples of an aquatic community, the sensitivity of β diversity with q = 0 ∼ 2 was examined. Whereas β diversity for q = 0 has a higher sensitivity to rare species drift (rapid exchanges or migration of many rare species) than do others, β diversity for q = 2 adequately responds to the variation or changes of dominant species. A scatterplot based on these two β diversity measures is proposed for the detailed examination of aquatic community dynamics. We show that the scatterplot is able to describe the monthly dynamics of dominant and rare species in the phytoplankton assemblage of a freshwater lake in Japan.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.099 | 0.051 |
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".