Bibliographic record
Abstract
The gut microbiome and aquatic toxicology: an emerging concept for environmental health Ondrej Adamovsky, Amanda N. Buerger, Alexis M. Wormington, Naomi Ector, Robert J. Griffitt, Joseph H. Bisesi Jr., and Christopher J. Martyniuk DOI: 10.1002/etc.4249 Best Paper Award co-winner Ondrej Adamovsky. Best Paper Award co-winner Amanda N. Buerger. Willie J.G.M Peijnenburg Dutch National Institute of Public Health and the Environment (RIVM) Bilthoven, The Netherlands Hummingbirds and bumble bees exposed to neonicotinoid and organophosphate insecticides in the Fraser Valley, British Columbia, Canada C.A. Bishop, A.J. Moran, M.C. Toshack, E. Elle, F. Maisonneuve, and J.E. Elliott DOI:10.1002/etc.4174. Review of atrazine sampling by polar organic chemical integrative samplers and Chemcatcher K. Booij and S. Chen DOI:10.1002/etc.4160 The acute toxicity of major ion salts to Ceriodaphnia dubia. III. Mathematical models for mixture toxicity R.J. Erickson, D.R. Mount, T.L. Highland, J.R. Hockett, D.J. Hoff, C.T. Jenson, T.J. Norberg-King, and K.N. Peterson DOI:10.1002/etc.3953 When significance becomes insignificant: Effect sizes and their uncertainties in Bayesian and frequentist frameworks as an alternative approach when analyzing ecotoxicological data A. Feckler, M. Low, J.P. Zubrod, and M. Bundschuh DOI:10.1002/etc.4127 Importance of growth rate on mercury and polychlorinated biphenyl bioaccumulation in fish J. Li, G.D. Haffner, G. Paterson, D.M. Walters, M.D. Burtnyk, and K.G. Drouillard DOI:10.1002/etc.4114 Evaluation of the use of bias factors with water monitoring data P.L. Mosquin, J. Aldworth, and W. Chen DOI:10.1002/etc.4154 Fate, uptake, and distribution of nanoencapsulated pesticides in soil–earthworm systems and implications for environmental risk assessment M.A. Mohd Firdaus, A. Agatz, M.E. Hodson, O.S. Al-Khazrajy, and A.B. Boxall DOI:10.1002/etc.4094. A framework for ecological risk assessment of metal mixtures in aquatic systems C. Nys, T. Van Regenmortel, C.R. Janssen, K. Oorts, E. Smolders, and K.A. De Schamphelaere DOI:10.1002/etc.4039 Understanding sources of methylmercury in songbirds with stable mercury isotopes: Challenges and future directions M.T. Tsui, E.M. Adams, A.K. Jackson, D.C. Evers, J.D. Blum, and S.J. Balogh DOI:10.1002/etc.3941 The combined and interactive effects of zinc, temperature, and phosphorus on the structure and functioning of a freshwater community D. Van de Perre, I. Roessink, C.R. Janssen, E. Smolders, F. De Laender, P.J. Van den Brink, and K.A. De Schamphelaere DOI:10.1002/etc.4201. Fate, uptake, and distribution of nanoencapsulated pesticides in soil–earthworm systems and implications for environmental risk assessment. Mohd Anuar Mohd Firdaus, Annika Agatz, Mark E. Hodson, Omar S.A. Al-Khazrajy, and Alistair B.A. Boxall Best Student Paper Award winner Mohd Anuar Mohd Firdaus.
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 | 0.001 | 0.006 |
| 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 | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.799 | 0.722 |
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".