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Record W7066227937

A global assessment of the temporal and spatial variability of national dilution factors

2012· other· en· W7066227937 on OpenAlexaboutno aff

Bibliographic record

VenueNERC Open Research Archive (Natural Environment Research Council) · 2012
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsDilutionSpatial variabilityPopulationSurface runoffHydrology (agriculture)EffluentSurface waterPer capita
DOInot available

Abstract

fetched live from OpenAlex

Of the many factors which influence the exposure of the freshwater aquatic environment to contaminating chemicals none has a more dramatic effect than dilution. Both where and when a chemical enters surface water will make an enormous difference to its impact on wildlife. This is of particular importance for "down-the-drain” chemicals as these substances are discharged to freshwaters via sewer systems after consumer use. However, too often, this dilution capacity is fixed to a “generic” value. Although the spatial variability of dilution factors is often acknowledged, temporal variability is often unaccounted for which may potentially lead to underestimating the environmental risk. To address the magnitude of these dilution differences across the world, estimates of dilution factors were developed globally at a 0.5° resolution using gridded data. Thus, the focus here is on the numbers and location of the human population and the river water available to dilute their waste. The river flows estimates are calculated at both annual and monthly resolution based on readily available annual and monthly runoff estimates. The domestic waste water effluent is derived from combining gridded population and national per capita domestic water use estimates. For each grid cell both annual and monthly dilution factors were generated. This approach allowed the quantification of temporal and spatial variability of dilution factors not only at a catchment level but also at a national level, by means of statistical measures such as median and percentiles. This method revealed the dramatic differences in available dilution of chemicals both within and between countries, for example Canada has on average 4-orders of magnitude more dilution available than Tunisia, and Finland 3-orders of magnitude more than Spain. Over the course of a year, national dilution could vary between 10 and a 1000-fold depending on the country. The work presented here is a significant step forward in terms of understanding the \nimpact of river flow temporal variability on dilution factors at a national and global scale. The proposed methodology has great potential for scientists and decision \nmakers across the globe, as it provides the means to improve screening-level chemical risk assessments.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.364
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2012
Admission routes1
Has abstractyes

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