Omic-style statistical clustering reveals old and new patterns in the Gulf of Maine ecosystem
Bibliographic record
Abstract
The burgeoning of omic technology has spawned a new subfield of statistics aimed at interpreting the complex information contained in omic data. Some of these statistical methods can be applied to any data set with taxonomic counts, and they have the potential to provide additional insights over traditional approaches. We test this potential by reanalyzing a well-studied zooplankton data set — the Gulf of Maine continuous plankton recorder series — using a modified Dirichlet-multinomial mixture (DMM) model. The data set has ∼50 years of approximately monthly samples along a transect from Boston, USA, to Yarmouth, Canada. The results from the DMM analysis were largely consistent with previous analyses but also provided new insights. Notably, the Calanus-dominated communities that returned following a reduction in the 1990s showed a loss of background diversity, suggesting a shift in sources and possibly higher vulnerability of these communities. The DMM analysis also revealed a breakdown of seasonal ecological succession in the 1990s. These changes could be a precursor to similar changes in other Calanus-dominated systems. The approach demonstrates a path toward linking traditional analyses with recent omic-style analyses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".