Bibliographic record
Abstract
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.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.034 |
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".