Bibliographic record
Abstract
WINNER OF THE 2015 BEST PAPER AWARD: Evaluating aquatic invertebrate vulnerability to insecticides based on intrinsic sensitivity, biological traits, and toxic mode of action Andreu Rico and Paul J. Van den Brink DOI: 10.1002/etc.3008 As a science, ecotoxicology has struggled to develop its “eco” side, relying on developing an understanding of the effects of toxic substances on natural ecosystems through the accumulation of indirect phenomenological evidence of the effects: what the eminent ecotoxicologist Guido Persoone described as “the effects of chemical A on species B under conditions C.” It is therefore refreshing when a paper attempts to move the field forward through the development of a predictive model of species sensitivity, through mining of this accumulated phenomenology—in the form of online databases. Rico and Van den Brink 1 build on previous studies in the ecological and ecotoxicological literature to extend our understanding of how the ecological, physiological, and morphological characteristics of taxa contribute to their sensitivity. What's new about their approach is the linkage of sensitivity mechanism (chemical risk) with taxon vulnerability in terms of behavioral adaptations to avoid or escape exposure (ecological risk). Applying trait knowledge to predict sensitivity is hardly new—the SpeAR approach (see Liess and Von der Ohe 2) attempted this, albeit less successfully, due to its lack of clear underlying sensitivity mechanisms. This said, Rico and Van den Brink make a more compelling case for the development of a predictive model of taxon sensitivity. Their results clearly support their assertions that data mining, coupled with access to high-quality toxicity and ecological information gleaned from hundreds of independent studies can yield greater insight into the reasons why some taxa persist while others decline in the face of exposure to specific classes of toxic substance. Best Paper Award winner Andreu Rico. Donald Baird University of New Brunswick Fredericton, New Brunswick, Canada The Gellyfish: An in situ equilibrium-based sampler for determining multiple free metal ion concentrations in marine ecosystems Zhao Dong, Christopher G. Lewis, Robert M. Burgess, and James P. Shine DOI: 10.1002/etc.2893 A review of mercury concentrations in freshwater fishes of Africa: Patterns and predictors Dalal E.L. Hanna, Christopher T. Solomon, Amanda E. Poste, David G. Buck, and Lauren J. Chapman DOI: 10.1002/etc.2818 Expanding Metal Mixture Toxicity Models to Natural Stream and Lake Invertebrate Communities Laurie S. Balistrieri, Christopher A. Mebane, Travis S. Schmidt, and Wendel (Bill) Keller DOI: 10.1002/etc.2824 Toxicity of sediments from lead-zinc mining areas to juvenile freshwater mussels (Lampsilis siliquoidea), compared to standard test organisms John M. Besser, Christopher G. Ingersoll, William G. Brumbaugh, Nile E. Kemble, Thomas W. May, Ning Wang, Donald D. MacDonald, and Andrew D. Roberts DOI: 10.1002/etc.2849 A New Bisphenol A Derivative for Estrogen Receptor Binding Studies with Surface Plasmon Resonance Wing-Leung Wong and Cheuk-Fai Chow DOI: 10.1002/etc.2939 [1] Parks AN, Chandler GT, Ho KT, Burgess RM, Ferguson PL. 2015. Environmental biodegradability of [14C] single-walled carbon nanotubes by Trametes versicolor and natural microbial cultures found in New Bedford Harbor sediment and aerated wastewater treatment plant sludge. Environ Toxicol Chem 34:247–251. DOI:10.1002/etc.2791. [2] Cupi D, Hartmann NB, Baun A. 2015. The influence of natural organic matter and aging on suspension stability in guideline toxicity testing of silver, zinc oxide, and titanium dioxide nanoparticles with Daphnia magna. Environ Toxicol Chem. 34:497–506. DOI:10.1002/etc.2855. [3] Konopka M, Henry HAL, Marti R, Topp E. 2015. Multi-year and short-term responses of soil ammonia-oxidizing prokaryotes to zinc bacitracin, monensin, and ivermectin, singly or in combination. Environ Toxicol Chem 34:618–625. DOI:10.1002/etc.2848. [4] Bauer AE, Frank RA, Headley JV, Peru KM, Hewitt LM, Dixon DG. 2015. Enhanced characterization of oil sands acid-extractable organics fractions using electrospray ionization–high-resolution mass spectrometry and synchronous fluorescence spectroscopy. Environ Toxicol Chem 34:1001–1008. DOI:10.1002/etc.2896. [5] Jeffries MKS, Stultz AE, Smith AW, Stephens DA, Rawlings JM, Belanger SE, Oris JT. 2015. The fish embryo toxicity test as a replacement for the larval growth and survival test: A comparison of test sensitivity and identification of alternative endpoints in zebrafish and fathead minnows. Environ Toxicol Chem 34:1369–1381. DOI:10.1002/etc.2932. [6] O'Reilly KT, Mohler RE, Zemo DA, Ahn S, Tiwary AK, Magaw RI, Espino Devine C, Synowiec KA. 2015. Identification of ester metabolites from petroleum hydrocarbon biodegradation in groundwater using GC×GC-TOFMS. Environ Toxicol Chem 34:1959–1961. DOI:10.1002/etc.3022. [7] Liu F, Wang WX. 2015. Linking trace element variations with macronutrients and major cations in marine mussels Mytilus edulis and Perna viridis. Environ Toxicol Chem 34:2041–2050. DOI:10.1002/etc.3027. [8] Eriksson KM, Johansson CH, Fihlman V, Grehn A, Sanli K, Andersson MX, Blanck H, Arrhenius Å, Sircar T, Backhaus T. 2015 Long-term effects of the antibacterial agent triclosan on marine periphyton communities. Environ Toxicol Chem 34:2067–2077. DOI:10.1002/etc.3030. [9] Soucek DJ, Dickinson A. 2015. Full-life chronic toxicity of sodium salts to the mayfly Neocloeon triangulifer in tests with laboratory cultured food. Environ Toxicol Chem 34:2126–2137. DOI:10.1002/etc.3038. [10] Kuchapski KA, Rasmussen JB. 2015. Surface coal mining influences on macroinvertebrate assemblages in streams of the Canadian Rocky Mountains. Environ Toxicol Chem 34:2138–2148. DOI:10.1002/etc.3052. [11] Silva ALP, Amorim MJB, Holmstrup M. 2015. Salinity changes impact of hazardous chemicals in Enchytraeus albidus. Environ Toxicol Chem 34:2159–2166. DOI:10.1002/etc.3058. [12] Roberts J, Bain PA, Kumar A, Hepplewhite C, Ellis DJ, Christy AG, Beavis SG. 2015. Tracking multiple modes of endocrine activity in Australia's largest inland sewage treatment plant and effluent- receiving environment using a panel of in vitro bioassays. Environ Toxicol Chem 34:2271–2281. DOI:10.1002/etc.3051. [13] Ilijin L, Mrdaković M, Todorović D, Vlahović M, Gavrilović A, Mrkonja A, Perić-Mataruga V. 2015. Life history traits and the activity of antioxidative enzymes in Lymantria dispar L. (lepidoptera, lymantriidae) larvae exposed to benzo[a]pyrene. Environ Toxicol Chem 34:2618–2624. DOI:10.1002/etc.3116. [14] Diamond J, Munkittrick K, Kapo KE, Flippin J. 2015. A framework for screening sites at risk from contaminants of emerging concern. Environ Toxicol Chem 34:2671–2681. DOI:10.1002/etc.3177. [15] Hao X, Cao Y, Zhang L, Zhang Y, Liu J. 2015. Fluoroquinolones in the Wenyu River catchment, China: Occurrence simulation and risk assessment. Environ Toxicol Chem 34:2764–2770. DOI:10.1002/etc.3158. [16] Liu J, Wang W-X. 2015. Reduced cadmium accumulation and toxicity in Daphnia magna under carbon nanotube exposure. Environ Toxicol Chem 34:2824–2832. DOI:10.1002/etc.3122. [17] Donald DB, Wissel B, Anas MUM. 2015. Species-specific mercury bioaccumulation in a diverse fish community. Environ Toxicol Chem 34:2846–2855. DOI:10.1002/etc.3130.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.445 | 0.328 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".