Eighth Annual Conference of inVIVO Planetary Health: From Challenges to Opportunities
Bibliographic record
Abstract
inVIVO Planetary Health (inVIVO) is a progressive scientific movement providing evidence, advocacy, and inspiration to align the interests and vitality of people, place, and planet. Our goal is to transform personal and planetary health through awareness, attitudes, and actions, and a deeper understanding of how all systems are interconnected and interdependent. Here, we present the abstracts and proceedings of our 8th annual conference, held in Detroit, Michigan in May 2019, themed "From Challenges, to Opportunities". Our far-ranging discussions addressed the complex interdependent ecological challenges of advancing global urbanization, including the biopsychosocial interactions in our living environment on physical, mental, and spiritual wellbeing, together with the wider community and societal factors that govern these. We had a strong solutions focus, with diverse strategies spanning from urban-greening and renewal, nature-relatedness, nutritional ecology, planetary diets, and microbiome rewilding, through to initiatives for promoting resilience, positive emotional assets, traditional cultural narratives, creativity, art projects for personal and community health, and exploring ways of positively shifting mindsets and value systems. Our cross-sectoral agenda underscored the importance and global impact of local initiatives everywhere by contributing to new normative values as part of a global interconnected grass-roots movement for planetary health.
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.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.089 | 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 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".