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Record W2766844220 · doi:10.1002/lob.10209

Navigating The Waters of Citizen Science: Lessons Learnt From a Pilot Lake Monitoring Project in Saskatchewan, Canada

2017· article· en· W2766844220 on OpenAlexaboutno aff
Lushani Nanayakkara, Jessica S. Bos, Kerri Finlay

Bibliographic record

VenueLimnology and Oceanography Bulletin · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsCitizen scienceQuality (philosophy)Political sciencePublic relationsEnvironmental resource managementEnvironmental science

Abstract

fetched live from OpenAlex

Citizen Science (CS) is a great way to get nonscientist stakeholders involved in research, improve scientific knowledge among community members, and help build support for research efforts. But the waters can be rough if this is your first foray into organizing and executing such an effort. We hope you find the lessons we learnt from our pilot project helpful! Jessica Bos training Citizen Scientists at one of the lakes in Saskatchewan, Canada. Map your route: Overview of the project In June 2017, we launched a pilot CS project at two lakes located in provincial parks in Saskatchewan (SK), Canada. The pilot project arose from interactions with local stakeholders: we gauged their interest in Citizen Science informally, while conducting previous research projects on-site and formally, from conducting lake-user surveys over the past few years (Nanayakkara and Wissel 2017, Nanayakkara et al. in prep). While we modelled our CS project after other successful existing programs (Lake Partner Program in Ontario, and LAKEWATCH in Florida, for example), we needed to modify the objectives and protocols to meet the local water quality issues and concerns. During the planning phase, we developed the organizational structure of the project, clearly defined the objectives of the project, networked with potential partners (both logistic and financial supporters), identified data needs, determined volunteer commitment and expectations, and explored strategies to help overcome challenges generally faced by CS groups. One such challenge is that data gathered by CS groups are often not used by researchers because of data quality issues (Conrad et al. 2011). To help ensure data quality and usability, our sampling regime included trained limnologists sampling the lakes within a few days of the CS volunteers. This way we could cross-check the data collected by volunteers and ensure data were comparable to those collected by professionally-trained staff. Ultimately, we decided the volunteers would collect clarity/turbidity measurements, water temperature, and pH data, chlorophyll samples, and water for nutrient, bacteria, and mineral analyses (performed by professionals in the lab). Before training the volunteers we held an information session about the current status of the lakes, what research has been/will be done at the lakes, aquatic invasive species, what a CS project entails, expectations of the CS volunteers, and commitment (time and effort) required of the volunteers. Additionally, we distributed a stakeholder survey to understand their lake-use patterns, concerns, and perceptions about the lakes. We also conducted an in-depth focus group at one site to understand stakeholder perspectives in more detail. So far we have experienced varying degrees of success regarding different aspects of the project. Following some critical self-evaluation, we believe the following considerations are essential to successfully navigating the waters of a CS initiative such as ours: Check the forecast: Gauge project-specific interest before you launch the project We cannot overstate the importance of stakeholder participation (as citizen scientists) to the success of that project. While the importance of stakeholder interest may seem obvious, the real lesson is that interest in participating in a hypothetical CS may not translate to participating in an actual project that requires time and effort. It is worth determining interest in participation throughout the project. For example, surveys can be conducted at key time points: in the beginning use it to gauge general interest in a CS project, but as the project details fall into place, inquire about willingness to commit to specific requirements. Of course, some coaxing and encouragement might be required at some sites; don't be discouraged by this, just be cognizant of the importance of stakeholder interest. The ideal scenario is a bottom-up approach where local community members have an innate interest in the lake; here you will be most likely to experience sustained interest through the lifetime of the project. We noticed a stark difference regarding interest between our two sites. At one, there is a very active community group who interact regularly with the lake, park employees, and visitors. This has led to a bottom-up organized engagement structure leading to more interest in participating in the CS project, with volunteers enthusiastically and effectively participating in the project. At the other site, in contrast, interest was top-down, with the Parks Manager expressing interest in the CS project. As a result, sustaining volunteer engagement and participation at this site has been more challenging. Where possible, interacting with local grass-roots community organizations (bottom-up) will result in more enthusiastic participation in CS, relative to sites where communication and coordination is conducted primarily with managers or government employees (top-down). Make sure all hands are on deck: Take the time to properly train volunteers (and re-train volunteers if necessary) We utilized several different training tools to teach the citizen scientists how to collect samples, including in-person training, online videos, and written protocol sheets. On-site volunteer training was conducted shortly after the information session, and each citizen scientist was shown how to collect each type of sample by a trained limnologist. We had the volunteers then repeat the sampling procedure while the trainer was present. This was crucial as many made little mistakes that were easily corrected by the trainer, which prevented confusion and concerns about data quality. Volunteers also had access to online how-to videos to review procedures before sampling, and we provided laminated step-by-step protocol sheets in the sampling kits that could be referenced while they were on the lake. It is also important to note that volunteers should not train other volunteers—this occurred at one of our lakes when the trainer was unavailable to meet for on-site training; this resulted in incorrect sampling from the new recruit. Like the children's game of “telephone,” these misunderstandings have compounded throughout the summer. In hindsight, a second training trip to review sampling protocols with volunteers would have corrected these sampling issues and provided us with more reliable data. Investing a little bit of extra time with volunteers will go a long way with regards to reliability of the samples! Respond to changing winds: Evaluate and modify sampling requirements/protocols as needed We originally developed the data collection protocols for the volunteers with the assumption that if volunteers actively processed some samples (e.g., filtering for chlorophyll) they would feel more directly involved in the research process. But as the pilot project has progressed, we have come to realize that it is more prudent to start with the basics and then work up to more complicated procedures, if volunteers express interest in this. When limnology sampling protocols are new to volunteers, going out in the water and doing secchi and temperature readings, and collecting water samples may be a sufficient starting point. Therefore, in the future we will start with the basic sampling outlined above and expand to chlorophyll filtering as volunteers become more comfortable with the protocols. Use your radio: Build working- relationships with key members of the community Another factor contributing to the success of the pilot project has been the support offered to us by key members of the community. At one site, the Parks Manager has provided us with invaluable logistic support including proper storage of samples and ensuring we receive the samples in a timely manner. While this level of support is certainly dependent on the infrastructure in place, it speaks to the benefits of developing strong working-relationships with such key members of the community and maintaining clear communication between all participants. Additionally, trusted community members or groups may be able to help spread the word about your project on your behalf. With our project, we put up fliers/posters around the parks, and advertised on social media sites, but this was not as effective as we anticipated. If we established a working-relationship with important community members beforehand, they may have been able to recruit more participants. Ultimately, if a group of interested community members approach you about a CS project, you are off to a great start! It might not always be smooth sailing, but if you are trying to understand the potential for a CS project (or maintaining one) at a particular site or community, we hope you find the above lessons helpful. Lushani Nanayakkara, lushanin@gmail.com, University of Regina, SK Canada Jessica Bos, University of Regina, SK Canada Kerri Finlay, University of Regina, SK Canada

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.273
Teacher spread0.248 · 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 teacher head, not a consensus.

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".

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Citations3
Published2017
Admission routes1
Has abstractyes

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