Delsa Workshop IV: Launching the Quantified Human Initiative
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.
Bibliographic record
Abstract
The mission of the Data-Enabled Life Sciences Alliance (DELSA Global) is to ''Accelerate the impact of data-enabled life science research on the pressing needs of the global society.''In its first 18 months, DELSA has catalyzed connections and interactions for more effective and sustainable science by bringing stakeholders together through physical or virtual proximity to share ideas, discuss new insights, and form novel collaborations.During our most recent annual Washington, DC, meeting (May 16-17, 2013), DELSA brought together life and computer scientists, data analysts, research funding agency representatives, and many others to discuss and formulate plans for furthering the initiative of 21st-century collective innovation.In an exciting day of lightning talks and brainstorming, participants discussed the management and analysis of emerging datasets that hold such immense promise for understanding and improving the human condition and our relationship with the worlds around us and within us.A focus of this meeting was on the Quantified Human (QH) Initiative.QH takes our natural curiosity about self and combines multi-omics and clinical data to draw conclusions about our physical condition both current and future.Measures such as height, weight, and blood pressure have been used throughout medical history; however, it is now possible to track many other measures such as caloric/nutritional intake and output, blood components, and sleep patterns.These data can be viewed in the context of our body as an ecosystem by including measures of the commensal microorganisms, collectively referred to as the microbiome.All of these results, taken together and over a period of time, can lead to a detailed picture of our overall health and open up a whole new level of understanding about the microenvironment that exists inside us.However, the resulting datasets are complex and immense.While the potential exists to use these data to explore the depth and breadth of ourselves in new and unimagined ways, we need new paradigms and policies for organizing, managing, and sharing the data, combined with new publishing and citation models.
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 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it