The community-based environmental monitoring systems as part of the research infrastructures underpinning the management of the coastal ecosystems towards the 2030 goals: the case of the citizen science observatory MINKA
Bibliographic record
Abstract
The MINKA citizen science observatory is a community-based platform dedicated to the environmental data collection as a contribution for the SDGs achievement. MINKA gathered data of coastal areas around the world of trained volunteers, currently about biodiversity, through geolocalized images uploaded by citizens.The photos are validated by means of a three stages process certified by experts toward the taxonomic identification; eventually the confirmed species and genera are available to users through data information facility infrastructures, such as Global Biodiversity Information Facility (GBIF). At the moment the MINKA platform is under an ongoing process improving the parameters according to the Essential Ocean Variables (EOVs) defined by the GOOS fundamental for assessments and saveguard ocean health. The integration of the citizen observatory MINKA with the research infrastructures of the MINKE, EU funded project focused on metrology research infrastructures to propose an innovative framework of ‘quality of oceanographic data’, underlined the complementarity of the citizen science data bringing completeness to the accuracy of the metrology research infrastructures of MINKE. Furthermore, the current requirements to fulfil the coastal ecosystem management plans, the international agreements, the United Nations Sustainable Development Goals or the Kunming- Montreal Global biodiversity framework of the Convention on Biological Diversity (CBD) requirements are asking for data and citizen science programs are contributing to essential biodiversity variables (EBV), essential ocean variables (EOV) and for SDGs as an important data source for new and non-traditional data (Chandler, M. et al, 2017; Fritz, S. et al., 2019; Woods, S.M., 2022). Recently MINKA has been included into the UN Acceleration Actions for SDGs platform due to the contributions to the Agenda 2030, mainly SDGs 11, 14, 15 and 17.
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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 | 0.026 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".