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Record W6921226055 · doi:10.6084/m9.figshare.95828

Elevated pCO2 causes developmental delay in early larval Pacific oysters, Crassostrea gigas

2012· article· en· W6921226055 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsCrassostreaPacific oysterLarvapCO2OysterOcean acidificationPacific ocean

Abstract

fetched live from OpenAlex

**This is a draft version of the following article: Timmins-Schiffman et al. (2012) Elevated pCO2 causes developmental delay in early larval Pacific oysters, Crassostrea gigas. Marine Biology, doi:10.1007/s00227-012-2055-x, which has been published in final form at the link provided below.** <br> Increasing atmospheric CO<sub>2</sub> equilibrates with surface seawater, elevating the concentration of aqueous hydrogen ions. This process, ocean acidification, is a future and contemporary concern for aquatic organisms, causing failures in Pacific oyster (<em>Crassostrea gigas</em>) aquaculture. This experiment determines the effect of elevated <em>p</em>CO<sub>2</sub> on the early development of <em>C. gigas </em>larvae from a wild Pacific Northwest population. Adults<em> </em>were collected from Friday Harbor, Washington, USA (48°31.7’ N, 12°1.1’ W) and spawned in July 2011. Larvae were exposed to Ambient (400 µatm CO<sub>2</sub>), MidCO<sub>2</sub> (700 µatm), or HighCO<sub>2</sub> (1000 µatm). After 24 hours, a greater proportion of larvae in the HighCO<sub>2 </sub>treatment were calcified as compared to Ambient. This unexpected observation is attributed to increased metabolic rate coupled with sufficient energy resources. Oyster larvae raised at HighCO<sub>2</sub> showed evidence of a developmental delay by 3 days post-fertilization, which resulted in smaller larvae that were less calcified.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.990

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)0.1410.011

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 head0.029
GPT teacher head0.222
Teacher spread0.192 · 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 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

Citations2
Published2012
Admission routes1
Has abstractyes

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