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Record W4235257361 · doi:10.4194/1303-2712-v19_7_08

[no title]

2018· article· en· W4235257361 on OpenAlexfundno aff
Gustavo Emilio Santos‐Medrano, Roberto Rico‐Martínez

Bibliographic record

VenueTurkish Journal of Fisheries and Aquatic Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsnot available
FundersUniversity of CambridgeInternational Development Research Centre
KeywordsDaphnia magnaDaphnia pulexBiologyPulexAcute toxicityToxicityBioindicatorBranchiopodaCladoceraOrganismZoologyToxicologyDaphniaEnvironmental chemistryEcologyChemistryCrustacean

Abstract

fetched live from OpenAlex

A comparison of acute toxicity (LC50 values) among nine toxicants: a) six metals: Cd, Cr, Cu, Hg, Pb, Ti, and b) three organics: benzene, ethyl acetate, and toluene, between the exotic cladoceran species Daphnia magna Straus 1820 and two native strains of freshwater cladoceran species: Daphnia pulex Leydig, 1860 and Simocephalus vetulus (O. F. Mller, 1776) was performed. We hypothesized that the exotic species would be less sensitive than native species. Our hypothesis was fulfilled. Daphnia magna was less sensitive than native species to eight of the nine compounds we analyzed. Results suggest that native species are better adjusted to local environmental conditions, and are more reliable as bioindicators of potential effects of toxicants on aquatic biota. Although the use of D. magna is recommended because of its ample toxicity database, some researchers propose the use of native species for toxicity tests. Therefore, to propose a native species to be considered as a test organism for official toxicity test for tropical countries, there is a need to increase the database of toxicants and to compare the sensitivities of several classes of toxicants with a model organism whose sensitivity to a broad variety of toxicants is well known, like D. magna.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.010
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.242
Teacher spread0.224 · 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 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

Citations8
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueTurkish Journal of Fisheries and Aquatic SciencesSame topicEnvironmental Toxicology and EcotoxicologyFrench-language works237,207