Exploring Space via Astromycology: A Report on the CIFAR Programs <i>Earth 4D</i> and <i>Fungal Kingdom</i> Inaugural Joint Meeting
Bibliographic record
Abstract
“Fungi on Mars!”: a popular news heading that piques public interest and makes scientists' blood boil. While such a statement is laden with misinformation and light on evidence, the search for past and present extraterrestrial life is an ongoing scientific effort. Moreover, it is one that is increasingly gaining momentum with the recent collection of martian rock cores from Jezero Crater by NASA's Perseverance rover. Despite the increasingly sophisticated approaches guiding the search for microbial life on other planets, fungi remain relatively underexplored compared to their bacterial counterparts, highlighting a gap between the astrobiological and fungal research communities. Through a meeting in April 2021, the CIFAR Earth 4D and Fungal Kingdom research programs worked to bridge this divide by uniting experts in each field. CIFAR is a Canadian-based global research organization that convenes researchers across disciplines to address important questions facing science and humanity. The CIFAR Earth 4D: Subsurface Science & Exploration and Fungal Kingdom: Threats & Opportunities research programs were launched by CIFAR in July 2019, each made up of approximately two dozen international researchers who are experts in their fields. The Earth 4D program, led by co-directors John Mustard (Brown University, USA) and Barbara Sherwood Lollar (University of Toronto, Canada), aims to understand the complex chemical, physical, and biological interactions that occur within and between Earth's surface and subsurface to explore questions on the evolution of planets and life. The Fungal Kingdom program, led by co-directors Leah Cowen (University of Toronto, Canada) and Joseph Heitman (Duke University, USA), seeks to tackle the most pressing threats fungi pose to human health, agriculture, and biodiversity and to harness their extraordinary potential. The programs met to explore areas for synergy within four major themes: (1) the origins of life; (2) the evolution and diversification of life; (3) life in diverse and extreme environments; and (4) extinction: lessons learned and threats. This report covers the research discussed during the meeting across these four themes.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".