Use of eBird as a tool for undergraduate education, research, and biodiversity conservation
Bibliographic record
Abstract
Birds have a long and complex relationship with humans on many levels, from food to culture and myth.Yet over the past 50 years, wild bird populations across the continental United States and Canada have declined by more than 25 percent.eBird is one of the world's largest biodiversity-related projects, accessible to both citizen-scientists and researchers.Data collected through eBird informs scientists on bird distribution, abundance, and habitat use both spatially and temporally.In this workshop, I will explain how eBird can also be applied at the level of undergraduate education to enrich students' understanding, appreciation, and knowledge of birds and their biodiversity.Participation in eBird enhances our human connection to birds.eBird was used as part of an online ornithology course at Saint Peter's University to provide an outside-the-classroom field-based lab experience, but it can also be applied to general biology, ecology, or other field study-based courses.It can be used throughout a semester or for a single laboratory experience.Various aspects of eBird will be explained from data entry to use of summary tools such as bar charts.Ways to develop bird identification skills will be discussed.Participants in this workshop will be able to practice using eBird on their smart phones through free apps provided by eBird and the Cornell Laboratory of Ornithology, such as Merlin Bird ID.Global eBird data is managed by the Cornell Lab of Ornithology and their partners to inform and benefit the cause of bird conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.012 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".