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Record W2117607513 · doi:10.1093/icesjms/fst049

The paradox of the “paradox of the plankton”

2013· article· en· W2117607513 on OpenAlexaff
Nicholas R. Record, Andrew J. Pershing, Frédéric Maps

Bibliographic record

VenueICES Journal of Marine Science · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsZooplanktonPlanktonBiodiversityDiversity (politics)EcosystemCompetitive exclusionEcologyPhytoplanktonTraitCompetition (biology)Perspective (graphical)Computer scienceBiologySociologyNutrientArtificial intelligence

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.191
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2013
Admission routes1
Has abstractyes

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