Citizen Science in High-Latitude Ecosystems : KdUINO Buoy to Support Data Gathering in Extreme Environments
Bibliographic record
Abstract
Citizen science in high latitude ecosystems: Testing Do-It-Yourself buoys in extreme environmentsIn recent years, participation in citizen science projects has grown and has become a complementary Earth Observing System.In the ocean sciences, scientists are also developing new methods based on Citizen Science.Some of the parameters of water simplest to understand by citizens are transparency and color.Volunteers from around the world currently are trained to create their own instruments to measure these parameters.These devices are known as Do-It-Yourself Technology.An example is the KdUINO buoy.With KdUINO, scientists estimate the diffuse attenuation coefficient, a parameter related to water transparency.In this paper, we study the feasibility of using the KdUINO under extreme environmental conditions.The goal is to place the buoys in high latitude areas where conventional measuring systems are harder to use.Volunteers tested KdUINOs in Canada and Antarctica.The proper operating of the buoy in those climates demonstrates that the KdUINO can be used for longterm studies in high latitude areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".