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Biological Stoichiometry

2017· other· en· W4231709192 on OpenAlexaff
Maren Striebel, Paul C. Frost, James J. Elser

Bibliographic record

VenueEncyclopedia of Life Sciences · 2017
Typeother
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsTrent University
Fundersnot available
KeywordsEcological stoichiometryTrophic levelOrganismPopulationElemental analysisEcosystemBiologyEcologyChemistry

Abstract

fetched live from OpenAlex

Abstract Biological stoichiometry is the study of the balance of energy and multiple chemical elements in living systems. It compares elemental requirements of organisms for growth, reproduction and maintenance with that provided by their nutritional resources. It considers the physiological, cellular and biochemical underpinnings of stoichiometric differences as well as their evolutionary basis. Primary producers generally exhibit greater flexibility in elemental composition compared to consumers, which leads to elemental imbalances between adjacent trophic levels. For individual organisms, the relatively low supply of an element can alter metabolic and physiological processes involving the acquisition, incorporation and release of multiple chemical elements. When sustained, elemental imbalances slow growth and limit reproduction of organisms, particularly those with relatively high elemental requirements. Elemental imbalances have been documented in diverse ecosystems and at multiple trophic levels and affect key ecological and evolutionary processes underlying population dynamics, life‐history evolution, community structure, trophic interactions and ecosystem function. Key Concepts Biological stoichiometry studies the balance of energy and multiple chemical elements in living systems. Biological stoichiometry compares the elemental compositions of resources with the elemental requirements of organisms. It also considers the environmental and evolutionary origins of elemental imbalances between producers and consumers. Biological stoichiometry approaches processes such as organism growth, population dynamics and trophic interactions as if such processes were composite chemical reactions that must simultaneously meet the law of mass conservation for multiple chemical elements and the rules of exact proportions in chemical reactions. Biological stoichiometry uses its elemental perspective on biochemical and physiological processes to understand intra‐ and interspecific interactions that involve the transfer or transformation of matter in food webs. Biological stoichiometry also provides a mechanistic framework for how animal species mediate ecosystem processes such as nutrient recycling. The stoichiometric approach can also be used to study trophic interactions as well as decomposition and microbial release of elements. Biological stoichiometry considers the molecular and evolutionary basis of major differences in the C:N:P ratios of living things. Understanding how evolution affects these ratios provides considerable insight into processes that link all levels of organisation in biology.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.020
GPT teacher head0.262
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2017
Admission routes1
Has abstractyes

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