Evidence map›Paper›PMID 36683605›Full record

ArticleHardwareX2023

Programmable Autonomous Water Samplers (PAWS): An inexpensive, adaptable and robust submersible system for time-integrated water sampling in freshwater and marine ecosystems.

Kyle C Neumann, Daniel La, Hyemin Yoo, Deron E Burkepile

Abstract read
In one paragraph

Article in HardwareX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Kyle C NeumannUC Santa Barbara, United States.
Daniel LaUC Santa Barbara, United States.
Hyemin YooUC Santa Barbara, United States.
Deron E BurkepileUC Santa Barbara, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Water chemistry conditions in freshwater and marine environments can change rapidly over both space and time. This is especially true in environments that are exposed to anthropogenic impacts such as sedimentation, sewage, runoff and other types of pollution. It is critical in studying these systems that researchers have tools capable of accurately collecting water samples across relevant spatial and temporal scales. Here we present an inexpensive, open-source Programmable Autonomous Water Sampler (PAWS) that is open source, compact, robust, highly adaptable and submersible to 40 m. PAWS utilizes a time-integrated sampling approach by collecting a single sample in a syringe slowly over minutes to days. Once analyzed, data from the sample collected represents and integrated average of water chemistry conditions over time. Due to its adaptability and low cost, PAWS has the potential to improve the spatial and temporal coverage of many freshwater and marine studies.

Indexed as

CoastalEcotoxicologyLimnologyNutrientsOceanographyWater chemistry

Identifiers

PMID36683605
PMCPMC9852790

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.