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Record W3209653966 · doi:10.1093/conphys/coab083

The role of Dynamic Energy Budgets in conservation physiology

2021· article· en· W3209653966 on OpenAlexafffund
Romain Lavaud, Ramón Filgueira, Starrlight Augustine

Bibliographic record

VenueConservation Physiology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsDalhousie University
FundersMarine Environmental Observation Prediction and Response Network
KeywordsBiologyConservation biologyEnvironmental resource managementEcologyBiodiversityEcosystemEnergy conservationEnvironmental planningRisk analysis (engineering)BusinessEnvironmental science

Abstract

fetched live from OpenAlex

The contribution of knowledge, concepts and perspectives from physiological ecology to conservation decision-making has become critical for understanding and acting upon threats to the persistence of sensitive species. Here we review applications of dynamic energy budget (DEB) theory to conservation issues and discuss how this theory for metabolic organization of all life on earth (from bacteria to whales) is well equipped to support current and future investigations in conservation research. DEB theory was first invented in 1979 in an applied institution for environmental quality assessment and mitigation. The theory has since undergone extensive development and applications. An increasing number of studies using DEB modelling have provided valuable insights and predictions in areas that pertain to conservation such as species distribution, evolutionary biology, toxicological impacts and ecosystem management. We discuss why DEB theory, through its mechanistic nature, its universality and the wide range of outcomes it can provide represents a valuable tool to tackle some of the current and future challenges linked to maintaining biodiversity, ensuring species survival, ecotoxicology, setting water and soil quality standards and restoring ecosystem structure and functioning in a changing environment under the pressure of anthropogenic driven changes.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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 designBench or experimental
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

Citations26
Published2021
Admission routes2
Has abstractyes

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