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Record W3133634205 · doi:10.3390/ani11030676

Using Google Trends to Determine Current, Past, and Future Trends in the Reptile Pet Trade

2021· article· en· W3133634205 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAnimals · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
Fundersnot available
KeywordsPopularityThreatened speciesLeopardGeographyBiologyZoologyEcologyPsychologyHabitat

Abstract

fetched live from OpenAlex

Reptiles are one of the most popular exotic pets in the world, with over a third of all described species currently being traded. However, the most commonly available reptiles are typically non-threatened, captive-bred, and/or domestically obtained, which means they are also largely unregulated and unmonitored, resulting in a large portion of the reptile pet trade remaining unknown. In this study, the past, current, and future trends of the most popular reptiles in the pet trade were examined. Google Trends was used to determine the global popularity of the most popular pets from 2004 to 2020 and compared to the results from an online survey sent to individuals involved in the reptile trade. The most popular pets from the previous five years were also compared globally across regions and countries. The results determined that the most popular reptile species during the last decade is by far bearded dragons, followed by ball pythons and leopard geckos. Although the survey results were similar when asked what the top reptiles were, most respondents named ball pythons as the most popular reptile. However, when asked what reptiles had lost the most popularity during the previous decade, the survey respondents named green iguanas, Burmese pythons, chameleons, red-eared sliders, and green anoles, concurring with what was found with Google Trends. The reptiles thought to be more popular in the upcoming decade by the survey participants were blue-tongued skinks, tegus, uromastyx, crested geckos, and ball pythons-most of which did indeed show an increase in popularity during the last decade, as indicated with Google Trends. The results from Google Trends demonstrated that ball pythons and crested geckos have increased their popularity more than any other reptile in the last two decades. Reptile popularity also differed between countries, with bearded dragons the most popular reptile in Australia, Western Europe, the U.S., and Canada. Leopard geckos were the most popular reptile in Italy and Turkey, and ball pythons were the reptile of choice in Mexico, Indonesia, and India. The general finding of this study is that the reptiles declining in popularity were mostly wild-caught or restricted due to regulations, while current and future species were captive-bred and available in many varieties or morphs. The most popular species were also docile, medium-sized, and easy to handle, with relatively simple care requirements. This study demonstrates that Google Trends can be a useful tool for determining relative popularity among reptiles, or any other pet group, with results closely mirroring those obtained through direct surveying of people involved in the pet trade. However, unlike surveys, this analysis is quick, quantifiable, and can show what is popular and in-demand not only at the global level but at much finer scales. Thus, Google Trends can be a valuable tool in many research applications, especially in topics that may otherwise be difficult to monitor and quantify.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.347
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it