A survey on aquatic animal keeping practices for home aquariums during the COVID-19 pandemic
Bibliographic record
Abstract
Human–animal interactions can generate a variety of benefits for the psychological and physiological wellbeing of humans. Therefore, more people may prefer to keep pets (such as aquatic animals) during stressful events, like the COVID-19 pandemic. In this study, an international survey of aquarium keepers was conducted to assess their attitudes toward home aquariums during the COVID-19 pandemic. Over 80% of the respondents, irrespective of gender, age, employment status, number of owned aquariums, or aquarium maintenance experience, confirmed that aquariums have produced stress-relieving benefits during the COVID-19 pandemic. Approximately, one-quarter of home aquarium owners claimed to have bought more than 15 fish and 15 aquatic invertebrates since the beginning of the stay-at-home restrictions. The majority of the respondents confirmed that their aquarium(s) was/were properly maintained during these regulations, particularly compared to the years prior to the COVID-19 pandemic. To some extent, a shortage of supply of live foods affected the maintenance performance of home aquariums.
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 | 0.001 |
| 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) | 0.001 | 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".