Future directions for understanding the coevolution of life and oxygen
Bibliographic record
Abstract
Abstract Our understanding of the coevolution of Earth’s surface environment and the biosphere is built on 50+ years of data collection and interpretation. Given the addition of data, and reinterpretations of mechanisms that drive observed long-term trends of planetary oxygenation, it is necessary to continually assess and critically review the status quo of our field in order to make meaningful progress as a global scientific community. Here we provide results of a survey, from globally distributed experts (n = 133; defined by a first author peer-reviewed publication between June 2017–2022, or co-authorship on several related peer-reviewed manuscripts) which was widely distributed during June-November 2022. This survey asked where our understanding of Earth’s oxygen history needs to be better developed and where our community should focus our efforts. Here we discuss avenues for future research, including key target intervals of Earth history, useful proxies that may require further development and/or a more nuanced section/sample-specific approach to data interpretation. Our hope is that this publication will stimulate future international collaboration and interdisciplinary research, whilst also providing support for funding grants that aim to investigate aspects of Earth history that lack clarity or are widely regarded as being poorly constrained.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".