Using the Google Trends tool to analyze searches for cyanobacteria
Bibliographic record
Abstract
Cyanobacteria carry out processes essential to sustaining life on Earth, such as oxygenic photosynthesis and nitrogen fixation. However, the excessive growth of their populations due to eutrophication and global warming can cause serious problems for the environment and humans. Because of the importance of quickly monitoring information about these organisms, digital tools such as Google Trends (GT), which monitors and stores all search history, are very useful. This study was conducted to investigate the temporal and seasonal patterns of searches for cyanobacteria using search data stored by GT. For this purpose, 10 countries with access to the Google platform were selected, searches were filtered by the term "cyanobacteria" in english and official languages, and the data collected covers the period from 2004 to September 2021. Most of the selected countries showed temporal search patterns in terms of interest in cyanobacteria regardless of language, with Brazil, Canada, India and Mexico standing out. In terms of seasonality, the flow of research between countries in tropical regions is associated with the drier seasons, such as spring and fall. For countries in temperate regions, this flow occurs during the seasons when the temperature rises. The relationships of interest in online research may change as other topics become relevant, but this can be reversed if these relevant points are identified and excluded.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.045 | 0.029 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".