2266 – N4U: a Web-based Gateway To Neuroimaging Research
Bibliographic record
Abstract
The unprecedented growth, availability and accessibility of sophisticated image analysis algorithms and powerful computational resources led to the idea of developing web-based computational infrastructures that could meet users’ new requirements. On the other hand the gap between the pace of data generation and the capability to extract clinically or scientifically relevant information is rapidly widening. Integration of the power of sophisticated mathematical models, efficient computational algorithms and advanced hardware infrastructure provides the necessary sensitivity to detect, extract and analyze subtle, dynamic and distributed patterns distinguishing one brain from another, and a diseased brain from a normal brain. neuGRID is the leading e-Infrastructure where neuroscientists can find core services and resources for brain image analysis. The neuGRID platform makes use of grid services and computing, and was developed with the final aim of overcoming the hurdles that the average scientist meets when trying to set up advanced experiments in computational neuroimaging, thereby empowering a larger base of scientists. Although originally built for neuroscientists working in the field of AD, the infrastructure is designed to be expandable to services from other medical fields ( e.g. multiple sclerosis, psychiatric conditions). “neuGRID for Users” will provide an e-Science environment by further developing and deploying the neuGRID infrastructure to deliver a Virtual Laboratory offering neuroscientists access to a wide range of datasets and algorithm pipelines, access to computational resources, services, and support. Information from this abstract is intended to make aware researchers working with neuroimaging of all possibilities when it comes to resources.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.019 |
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 teacher head, 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".