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Record W2069225761 · doi:10.1897/03-369

Morphological responses of <i>Daphnia pulex</i> to <i>Chaoborus americanus</i> kairomone in the presence and absence of metals

2004· article· en· W2069225761 on OpenAlexaff
Kim Hunter, Greg G. Pyle

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2004
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsNipissing University
Fundersnot available
KeywordsDaphnia pulexKairomonePulexDaphniaBranchiopodaZoologyBiologyCladoceraEcologyCrustaceanPredation

Abstract

fetched live from OpenAlex

Daphnia pulex neonates develop neck teeth in the presence of predatory kairomone from Chaoborus americanus that are fed D. pulex. These neck teeth reduce the susceptibility of the neonates to predation. Evidence suggests that aqueous metals interfere with chemical communication in fish. The objective of our study was to determine if Cu or Ni at environmentally relevant concentrations affects predatory kairomone response in D. pulex. To test this possibility, D. pulex were placed in increasing waterborne concentrations of Cu or Ni in the presence or absence of predatory kairomone. Both Cu and Ni reduced neck tooth induction in D. pulex neonates in the presence of predatory kairomone. Copper had a significant nonlinear effect on neck tooth length consistent with a hormetic response, where neck tooth length was highest at 5 microg/L Cu, but not significantly different than 0 microg/L Cu at higher Cu concentrations. A Ni concentration of 200 microg/L caused D. pulex to become hypersensitive to Chaoborus regardless of Chaoborus' diet, leading to increased neck tooth number but decreased neck tooth length. Neither Ni nor Cu produced any significant effects on body length or brood size. These results suggest that metal inhibition of neck tooth induction probably occurs along the signal transduction pathway. Impairment of chemosensory response to predatory chemical cues may have widespread ecological consequences in aquatic systems contaminated by metals.

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 score0.064
Threshold uncertainty score0.409

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 studies0.0000.001
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 head0.047
GPT teacher head0.252
Teacher spread0.205 · 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

Citations58
Published2004
Admission routes1
Has abstractyes

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