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Record W2124238755 · doi:10.1098/rsbl.2006.0598

Genetically enhanced growth causes increased mortality in hypoxic environments

2007· article· en· W2124238755 on OpenAlexaff
Line Elisabeth Sundt-Hansen, L. Fredrik Sundström, Sigurd Einum, Kjetil Hindar, Ian Fleming, Robert H. Devlin

Bibliographic record

VenueBiology Letters · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMemorial University of NewfoundlandBC Research (Canada)
FundersNorges Forskningsråd
KeywordsBiologyOncorhynchusLarvaHypoxia (environmental)GenotypeZoologyTransgeneEcologyAnimal scienceOxygenGeneGeneticsFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Rapid growth and development are associated with several fitness-related benefits. Yet, organisms usually grow more slowly than their physiological maximum, suggesting that rapid growth may carry costs. Here we use coho salmon (Oncorhynchus kisutch) eggs of wild and transgenic genotypes to test whether rapid growth causes reduced tolerance to low levels of oxygen (hypoxia). Eggs were exposed to four different durations of hypoxia, and survival and growth were recorded until the end of the larval stage. Survival rates decreased with increasing duration of hypoxia, but this decrease was most pronounced for the transgenic group. Larval mass was also negatively affected by hypoxia; however, transgenic genotypes were significantly larger than wild genotypes at the end of the larval stage. Oxygen can be a limiting factor for survival and development in a wide range of organisms, particularly during the egg stage. Thus, the reduced ability of fast-growing genotypes to cope with low oxygen levels identified in the present study may represent a general constraint on evolution of rapid growth across taxa.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.496

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 science0.0000.000
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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

Citations39
Published2007
Admission routes1
Has abstractyes

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