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Record W4399023229 · doi:10.1177/00368504241257040

Community science for assessing the vulnerability of freshwater ecosystems: Water quality monitoring, restoration, and outreach by young naturalists in Nova Scotia, Canada

2024· article· en· W4399023229 on OpenAlexfundaboutno aff
Heather Cray, Becky Parker

Bibliographic record

VenueScience Progress · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersEcho Foundation
KeywordsOutreachCitizen scienceWater qualityEnvironmental resource managementGeographyEnvironmental planningEnvironmental scienceEcologyPolitical scienceBiology

Abstract

fetched live from OpenAlex

Community science models that complement formal scientific investigations are valuable tools for addressing gaps in knowledge and engaging the community. Freshwater quality monitoring and vulnerability assessment, for example, are essential for conserving freshwater ecosystems, but often suffer from limited resources. While municipal programs focus on priority areas, community-based models can improve overall coverage both spatially and temporally. As an example of how community science initiatives centered on freshwater ecosystems enhance monitoring capacity, we present the case of the Young Naturalists Club (YNC) in Nova Scotia, Canada. The YNC's Nature Guardians program involves youth aged 10-14 in water monitoring, restoration, and outreach activities within Shubie Park, in Dartmouth, Nova Scotia. Between 2018 and 2021 the Nature Guardians collected water monitoring data at multiple park locations, and shared findings with city authorities and the Atlantic Water Network. In response to high bacteria counts, and concerns over nutrient influxes, the group's 2021-2023 restoration efforts have aimed to improve water quality, focusing on native plantings and outreach signage. This type of community-based monitoring offers several advantages, including local site selection based on community concerns, the potential for low-cost long-term monitoring, and community engagement. While a community-based monitoring model presents certain challenges including data standardization and verification, it offers a broader reach and can produce high-quality data when appropriate protocols are followed. This case underscores the potential of a community-based water quality monitoring approach and highlights the potential for community science to augment existing assessment structures, ultimately contributing to more resilient and sustainable freshwater ecosystems.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.689

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.002
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.357
Teacher spread0.313 · 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.

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
Published2024
Admission routes2
Has abstractyes

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