Evidence map›Paper›PMID 28828562›Full record

ArticleEnvironmental monitoring and assessment2017

The application of high temporal resolution data in river catchment modelling and management strategies.

L Crockford, S O'Riordain, D Taylor, A R Melland, G Shortle, P Jordan

Abstract read
PubMed Publisher
In one paragraph

Article in Environmental monitoring and assessment, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

L CrockfordThe Agricultural Catchments Programme, Teagasc, Johnstown Castle, Wexford, Ireland. lcrockford@harper-adams.ac.uk.ORCID http://orcid.org/0000-0001-8336-4149
S O'RiordainStatistics, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland.
D TaylorDepartment of Geography, National University of Singapore, Singapore, Singapore.
A R MellandNational Centre for Engineering in Agriculture, University of Southern Queensland, Queensland, Australia.
G ShortleThe Agricultural Catchments Programme, Teagasc, Johnstown Castle, Wexford, Ireland.
P JordanThe Agricultural Catchments Programme, Teagasc, Johnstown Castle, Wexford, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modelling changes in river water quality, and by extension developing river management strategies, has historically been reliant on empirical data collected at relatively low temporal resolutions. With access to data collected at higher temporal resolutions, this study investigated how these new dataset types could be employed to assess the precision and accuracy of two phosphorus (P) load apportionment models (LAMs) developed on lower resolution empirical data. Predictions were made of point and diffuse sources of P across ten different sampling scenarios. Sampling resolution ranged from hourly to monthly through the use of 2000 newly created datasets from high frequency P and discharge data collected from a eutrophic river draining a 9.48 km

Indexed as

Models, TheoreticalEnvironmental MonitoringFresh WaterPhosphorusRiversWater MovementsWater PollutionWater QualityPhosphorusAgricultureHigh frequency dataModellingPhosphorusPollutionWater quality

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.