The observation of birds as informative tools to develop citizen science: Contributions of a 10-year record in the state of Chihuahua, Mexico
Bibliographic record
Abstract
Abstract This article analyses historical data from observations made to birds in breeding, throughout two routes with urban characteristics and during a consecutive period of 10 years (2009-2018), following a precise methodology designed by the North American Breeding Bird Survey. The analyzed routes are officially registered in the Mexican Commission for Biodiversity’s Knowledge and Use, the United States Geological Survey Patuxent Wildlife Research Center, and the Canadian Wildlife Service Research Centre. The observations were made by citizens without formal professional education; hence the results may be considered within the framework of citizen science. Their contributions provided important data for decision-making regarding environmental issues, since the presence of birds is considered one of the main indicators on health conditions of an ecosystem. Data analysis identified two basic conditions: (i) a reduction of the 23% in the number of species found, as many of them disappeared during counting; and (ii) the significant increase in population of other species, three of pigeons among them. Apart from the study of bird behavior in the routes with urban characteristics, the article acknowledges the lack of connection and use of the information produced from monitoring for decision-making and education regarding environmental issues. Therefore, we consider crucial to create scientific observatories, both available to experts in the field and to the general population, as the ultimate purpose would be the production of citizen science.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".