Citizen Science Programs as Scientific Data Collection Approach of Soil Medium
Bibliographic record
Abstract
Community-engaged data collection and research, known as citizen science, is becoming increasingly popular in modern research. Citizen science programs, using social media platforms, provide an efficient means of rapidly gathering substantial and relevant data for scientific inquiries in a cost-effective manner. In Hungary, the first citizen science program, titled "Life in Undies”, was launched in 2021 by the Institute for Soil Sciences. This initiative, inspired by the Canadian "Soil your Undies" challenge and other similar initiatives around the world, focused on collecting soil data by surveying the decomposition of cotton underpants. The percentage decomposition of the cotton underwear serves as an indirect indicator of soil health and contributes to the creation of a map illustrating seasonal microbiological activity in the soil. The second, ongoing citizen science program is called "InvestiGATE for Your Soil". This initiative continuously collects primary soil data, including pH, CaCO3 content, soil texture, thickness of surface humic layer by easy measurement methods carried out with household tools following a strict tutorial. The aim is to build a comprehensive and diverse dataset of proxy variables, facilitating the creation of thematic soil. In addition to the short introduction of the two initiatives, our poster will highlight innovations implemented in data collection to improve the efficiency of the data cleaning process. Validation mechanisms have been incorporated to ensure the reliability of the collected data, contributing to the success not only of these citizen science programs but also of others. Our poster will showcase the outcomes of these citizen science programs, featuring: A thematic map illustrating soil microbiological activity in the spring of 2021, derived from over a thousand data points collected nationwide. A preliminary thematic map depicting key soil properties from our continually expanding database generated by the ongoing citizen science program. Acknowledgement: This work has been carried out with the support of the Hungarian National Research, Development and Innovation Office K-131820 together with MEC N-140646.
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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 | 0.069 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".