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View of the CERN Antiproton Decelerator (AD) and portrait of Prof. Tommy Eriksson, in charge of the AD machine.

2011· article· en· W31271998 on OpenAlexfundno aff
Maximilien Brice

Bibliographic record

VenueChemosphere · 2011
Typearticle
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPortraitLarge Hadron ColliderPhysicsCharge (physics)AntiprotonNuclear physicsArt historyArtParticle physicsProton

Abstract

fetched live from OpenAlex

The sensory-motor function in larval zebrafish (Danio rerio) following exposure to low water pH was investigated. The results suggested that acid exposure (pH 4.0-5.0; control: pH 7.4) significantly reduced the touch-evoked escape response of larval zebrafish at 3 days post fertilization (dpf). A significant number of pH 4.0-exposed larvae also exhibited a lack of escape response. Treatment with neurotransmitters showed that serotonin or acetylcholine, but not dopamine, reduced the adverse effects of acid exposure on the escape response of larvae. Co-exposure to serotonin and acetylcholine did not further improve the escape response of acid-exposed larvae, suggesting no additive effect by these neurotransmitters. Interestingly, the negative effects of acid exposure on the escape response could be completely rescued by elevating the water levels of Ca<sup>2+</sup>, but not NaCl. Collectively, these results suggested that acid-induced disruption in Ca<sup>2+</sup> balance suppressed the serotonin- and acetylcholine-mediated neuronal signaling, thereby affecting the sensory-motor function and escape response of larval zebrafish. Findings from the present study may have important implication for the survival (e.g., escape from adverse conditions) of larval fish in acid-impacted environments, particularly during early development when they are still incapable of spontaneous swimming.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.321

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 head0.013
GPT teacher head0.189
Teacher spread0.176 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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