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Threshold elemental ratios of carbon and phosphorus in aquatic consumers

2006· letter· en· W2159207405 on OpenAlexaff
Paul C. Frost, Jonathan P. Benstead, Wyatt F. Cross, Helmut Hillebrand, James H. Larson, Marguerite A. Xenopoulos, Takehito Yoshida

Bibliographic record

VenueEcology Letters · 2006
Typeletter
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsTrent University
Fundersnot available
KeywordsEcological stoichiometryDetritivoreBioenergeticsPhosphorusInvertebrateEcologyBiologyNutrientNutrient cycleCarbon fibersEnvironmental chemistryChemistry

Abstract

fetched live from OpenAlex

Inadequate supply of one or more mineral elements can slow the growth of animal consumers and alter their physiology, life history and behaviour. A key concept for understanding nutrient deficiency in animals is the threshold elemental ratio (TER), at which growth limitation switches from one element to another. We used a stoichiometric model that coupled animal bioenergetics and body elemental composition to estimate TER of carbon and phosphorus (TER(C:P)) for 41 aquatic consumer taxa. We found a wide range in TER(C:P) (77-3086, ratio by atoms), which was generated by interspecific differences in body C : P ratios and gross growth efficiencies of C. TER(C:P) also varied among aquatic invertebrates having different feeding strategies, such that detritivores had significantly higher threshold ratios than grazers and predators. The higher TER(C:P) in detritivores resulted not only from lower gross growth efficiencies of carbon but also reflected lower body P content in these consumers. Supporting previous stoichiometric theory, we found TER(C:P) to be negatively correlated with the maximum growth rate of invertebrate consumers. By coupling bioenergetics and stoichiometry, this analysis revealed strong linkages among the physiology, ecology and evolution of nutritional demands for animal growth.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0020.001
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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations360
Published2006
Admission routes1
Has abstractyes

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