The Ecological Genome Project and the Promises of Ecogenomics for Society: Realising a Shared Vision as One Health
Bibliographic record
Abstract
This paper develops a vision for The Ecological Genome Project: an aspirational, global endeavour to connect human genomic sciences with the ethos of ecological sciences. The Project's goal is to strengthen interdisciplinary networks that relate to diverse initiatives using genomic technologies, with respect to shared ethical frameworks and governance structures. To this end, this paper proposes a practical definition of ecogenomics to align various methodologies and values in a single environmental field using principles used to safeguard all forms of life in their habitats. We achieve this by using a One Health approach as a pretext for disparate disciplines to collaborate and also a lens to view the Ethical, Legal and Social Implications (ELSI) inherent in ecological systems.
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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 | 0.082 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.067 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".