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Record W2066457817 · doi:10.1086/430036

Humic Substances Influence Sodium Metabolism in the Freshwater Crustacean<i>Daphnia magna</i>

2005· article· en· W2066457817 on OpenAlexaff
Chris N. Glover, Eric F. Pane, Chris M. Wood

Bibliographic record

VenuePhysiological and Biochemical Zoology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDaphnia magnaSodiumHumic acidBiotaEnvironmental chemistryChemistryMetabolismToxicityDaphniaBiologyCrustaceanBiochemistryEcology

Abstract

fetched live from OpenAlex

Humic substances are ubiquitous components of natural waters with important roles in alleviating metal toxicity to aquatic organisms. Recent literature reports suggest that humic substances may also exert direct influences on biota. This study investigated the influence of two commercially available humic substances on sodium metabolism in Daphnia magna, a hyperregulating freshwater crustacean. Environmentally realistic levels of Suwannee River natural organic matter (SRN) and Aldrich humic acid (AHA) significantly enhanced sodium transport. This effect was described as an uncompetitive stimulation of sodium influx, as characterised by an increased maximal sodium transport rate (Jmax), accompanied by a decreased uptake affinity (increased Km). SRN exposure also significantly promoted the unidirectional loss of sodium from the daphnids to the water, an effect not observed in the presence of AHA. A 24‐h preexposure to AHA before influx measurement had no effect on AHA‐induced stimulation of sodium influx. Conversely, 24‐h preexposure to SRN resulted in influx values that returned to control (humic‐free) levels. Whole‐body sodium levels reduced by SRN exposure were also restored to control levels following 24‐h SRN preexposure. The significance and potential mechanisms of these actions are discussed, and the toxicological implications of these findings are assessed.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.999

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.217
Teacher spread0.207 · 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.

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

Citations72
Published2005
Admission routes1
Has abstractyes

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