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Metal Mixture Modeling Evaluation project: 2. Comparison of four modeling approaches

Overview of attention for article published in Environmental Toxicology & Chemistry, February 2015
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

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2 tweeters

Citations

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37 Dimensions

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Title
Metal Mixture Modeling Evaluation project: 2. Comparison of four modeling approaches
Published in
Environmental Toxicology & Chemistry, February 2015
DOI 10.1002/etc.2820
Pubmed ID
Authors

Kevin J. Farley, Joseph S. Meyer, Laurie S. Balistrieri, Karel A. C. De Schamphelaere, Yuichi Iwasaki, Colin R. Janssen, Masashi Kamo, Stephen Lofts, Christopher A. Mebane, Wataru Naito, Adam C. Ryan, Robert C. Santore, Edward Tipping

Abstract

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the U.S. Geological Survey (USA), HDR׀HydroQual, Inc. (USA), and the Centre for Ecology and Hydrology (UK) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME Workshop in Brussels, Belgium (May 2012), is provided herein. Overall, the models were found to be similar in structure (free ion activities computed by WHAM; specific or non-specific binding of metals/cations in or on the organism; specification of metal potency factors and/or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single versus multiple types of binding site on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong inter-relationships among the model parameters (log KM values, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 3%
Unknown 34 97%

Demographic breakdown

Readers by professional status Count As %
Unspecified 8 23%
Researcher 8 23%
Student > Ph. D. Student 6 17%
Student > Master 5 14%
Professor 4 11%
Other 4 11%
Readers by discipline Count As %
Environmental Science 14 40%
Unspecified 9 26%
Agricultural and Biological Sciences 6 17%
Biochemistry, Genetics and Molecular Biology 2 6%
Earth and Planetary Sciences 2 6%
Other 2 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 July 2018.
All research outputs
#7,900,445
of 13,253,522 outputs
Outputs from Environmental Toxicology & Chemistry
#2,509
of 3,843 outputs
Outputs of similar age
#135,415
of 294,826 outputs
Outputs of similar age from Environmental Toxicology & Chemistry
#28
of 69 outputs
Altmetric has tracked 13,253,522 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,843 research outputs from this source. They receive a mean Attention Score of 4.5. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 294,826 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.