An innovation of two established methods for monitoring water colour and clarity: participatory science using the mini- and midi- secchi disks
Bibliographic record
Abstract
Measurements of water colour and clarity are used to track the environmental status of lakes, estuaries and oceans. The oldest standardised methods for measuring water clarity and colour are the Secchi disk and Forel-Ule colour scale. Both techniques were developed in the 19th century and require use of the human eye. Despite the advent of optoelectronic-based sensing, these visual methods are still used today, owing to their sensitivity, affordability, simplicity and long history of use. Recently, a hand-held device was developed for measuring the Secchi depth and Forel-Ule colour (presented in two formats, named the mini- and midi-Secchi disk). Designed to be small, light and convenient-to-use, it is well suited for participatory science projects that involve monitoring water colour and clarity. To date, over 900 mini- and midi-Secchi disks have been distributed to citizens and scientists, primarily through six projects, with data mostly transferred via mobile phone applications to data servers and dashboards. In this paper, we describe the methods used in the projects and show some characteristics of the datasets collected so far. We showcase how the device can be used for scientific applications, such as verifying satellite data products, gaining new scientific insights, and supporting public engagement and education. Finally, we provide suggestions for methodological improvements and future developments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".