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Record W2092805848 · doi:10.4031/002533203787537339

Mortality of Zooplankton and Invertebrate Larvae Exposed to Cyclonic Pre-Treatment and Ultraviolet Radiation

2003· article· en· W2092805848 on OpenAlexfundno aff
T.F. Sutherland, Colin D. Levings, Sven Petersen, W.W. Hesse

Bibliographic record

VenueMarine Technology Society Journal · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsZooplanktonBiologyCladoceraPlanktonPopulationBrine shrimpLarvaInvertebrateUltraviolet radiationEcologyFisheryChemistryMedicine

Abstract

fetched live from OpenAlex

Experiments were carried out to determine the effect of an integrated ballast water treatment system on the mortality of zooplankton. This treatment system consists of 2 treatment stages: 1) the cyclonic pretreatment phase and: 2) the ultraviolet radiation phase. Various zooplankton species were exposed to the treatment system over a range of 6 ultraviolet dosages (UV-C). In general, both treatment phases exhibited significant effects on zooplankton mortality. The results revealed that clam, mussel, and oyster larvae exhibited statistically similar mortality thresholds ranging between 96% and 99% at the higher UV dosages. A significant difference was observed when comparing these results to the mortality thresholds of brine shrimp nauplii (77%) and a natural population of zooplankton (40%). These findings suggest that species grouped into similar taxa may exhibit similar mortality responses to ultraviolet radiation, while those taxa containing different structural tissues capable of different photoprotective properties may exhibit different mortality responses. In addition, time-dependent, post-treatment mortality observations varied according to the taxa present, demonstrating that biological assays should include time-dependent mortality estimates to accurately assess the efficiencies of ballast water treatment systems.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.906

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)0.0010.000

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 head0.224
Teacher spread0.216 · 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

Citations25
Published2003
Admission routes1
Has abstractyes

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