Googling Trends in Conservation Biology
Bibliographic record
Abstract
Web-crawling approaches, that is, automated programs data mining the internet to obtain information about a particular process, have recently been proposed for monitoring early signs of ecosystem degradation or for establishing crop calendars. However, lack of a clear conceptual and methodological framework has prevented the development of such approaches within the field of conservation biology. Our objective was to illustrate how Google Trends, a freely accessible web-crawling engine, can be used to track changes in timing of biological processes, spatial distribution of invasive species, and level of public awareness about key conservation issues. Google Trends returns the number of internet searches that were made for a keyword in a given region of the world over a defined period. Using data retrieved online for 13 countries, we exemplify how Google Trends can be used to study the timing of biological processes, such as the seasonal recurrence of pollen release or mosquito outbreaks across a latitudinal gradient. We mapped the spatial extent of results from Google Trends for 5 invasive species in the United States and found geographic patterns in invasions that are consistent with their coarse-grained distribution at state levels. From 2004 through 2012, Google Trends showed that the level of public interest and awareness about conservation issues related to ecosystem services, biodiversity, and climate change increased, decreased, and followed both trends, respectively. Finally, to further the development of research approaches at the interface of conservation biology, collective knowledge, and environmental management, we developed an algorithm that allows the rapid retrieval of Google Trends data.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.107 | 0.003 |
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".