Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,011 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,445 | 0,328 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».