The paradox of the “paradox of the plankton”
Bibliographic record
Abstract
Abstract Record, N. R., Pershing, A. J., and Maps, F. 2014. The paradox of the “paradox of the plankton”. – ICES Journal of Marine Science, 71: 236–240. One of the central orienting questions in biodiversity theory and ecology is the “paradox of the plankton”, which asks how it is possible for many species to coexist on limited resources given the tendency for competition to exclude species. Over the past five decades, ecologists have offered dozens of solutions to the paradox, invoking game theory, chaos, stochastics, and many other concepts. Despite the plentitude of solutions to the paradox, ecologists continue to offer up novel solutions. Ocean modellers are now faced with the opposite paradox: given the overabundance and the diversity of solutions to the paradox, what is the appropriate way to build coexistence into ecosystem models? Ocean ecosystem models have a very standardized form—nutrient–phytoplankton–zooplankton (NPZ)-type systems of differential equations—where competitive exclusion is a common model behaviour. We suggest approaching the problem from the perspective of community-level patterns. We offer a prototype for building coexistence into NPZ models. The model allows for diverse assemblages of phytoplankton or zooplankton groups to persist and produces accurate community-level patterns. The approach is simple, adding only one additional parameter, and allows us to test the effects of trait distributions and environmental variables on diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".