The Two Frontiers Project Field Handbook and OpenTools: Standardizing microbial fieldwork for biobank-scale sequencing and culturomics
Bibliographic record
Abstract
Abstract From medicines to materials, our planet’s microbial diversity comprises an enormous wellspring of biotechnological potential. For centuries, microbiologists have developed tools for interrogating microbial function, ranging from microscopy and culturing to, more recently, metagenomics. However, deploying these tools during fieldwork requires substantial forward planning, interdisciplinary technical expertise, and plans for navigating permitting and the ethical implications of bioprospecting. To address these challenges, we built The Two Frontiers Project Handbook and OpenTools Resource, which aggregates our expertise in high-throughput sampling, sequencing, and culturing of microbes from thousands of samples. We provide our full suite of fieldwork methods as well as relevant software and hardware. We lay our standards for team roles and construction, general expedition planning, sample transport, permitting, and numerous other key aspects of executing a successful field campaign. The version-controlled resource is available at https://two-frontiers-project.github.io/ and is open for non-commercial use.
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 | 0.037 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.046 |
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".