Identify scientific org flags
using AI
Below is a free classifier to identify scientific org flags. Just upload your image, and our AI will predict the scientific organization represented by the flag - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("scientific-org-flags-identifier", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/scientific-org-flags-identifier/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_image_url"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_image_url"}' \
https://www.nyckel.com/v1/functions/scientific-org-flags-identifier/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict the scientific organization represented by the flag.
This pretrained image model uses a Nyckel-created dataset and has 48 labels, including American Chemical Society, American Physical Society, California Institute Of Technology, Caltech, Cambridge University Press, Carnegie Institution, Chinese Academy Of Sciences, Council Of Scientific And Industrial Research, European Molecular Biology Laboratory and France National Center For Scientific Research.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the scientific organization represented by the flag).
Whether you're just curious or building scientific org flags detection into your application, we hope our classifier proves helpful.
Related Classifiers
Need to identify scientific org flags at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Research Integrity Assurance: This use case involves using the false image classification function to identify manipulated or misleading images in scientific publications. By flagging dubious images, research institutions can uphold integrity and support the credibility of published findings.
- Peer Review Enhancement: Scientific journals can implement the identifier to enhance the peer review process. Reviewers can receive alerts when submitted articles contain potentially falsified images, leading to more thorough evaluations and improved publication standards.
- Grant Application Scrutiny: Funding agencies can employ the false image classification function to vet applications for research grants. Identifying false images can help ensure that grants are awarded based on genuine and reliable data, thus optimizing the allocation of research funds.
- Educational Content Verification: Educational platforms and knowledge dissemination organizations can use the identifier to verify images used in their content. This ensures that learners are not exposed to false representations of scientific data, fostering accurate understanding and knowledge.
- Compliance Monitoring: Regulatory bodies in industries such as pharmaceuticals can use the technology for compliance monitoring. By identifying false images in drug marketing materials, agencies can ensure adherence to regulatory standards and prevent misinformation in advertising.
- Data Integrity in AI Training: Organizations developing AI systems can utilize the classification function during the data collection phase. By filtering out images that may skew training data, they can enhance the robustness and reliability of AI models used in various applications.
- Social Media Misinformation Reduction: Social media platforms can integrate the identifier to flag scientific images that may have been falsely presented. This can help combat misinformation related to scientific research and impact public understanding of important health and environmental topics.