Ideas and perspectives: Biogeochemistry – some key foci for the future
Bibliographic record
Abstract
Biogeochemistry has an important role to play in many environmental issues of current concern related to global change and air, water, and soil quality. However, reliable predictions and tangible implementation of solutions, offered by biogeochemistry, will need further integration of disciplines. Here, we refocus on how further developing and strengthening ties between biology, geology, chemistry, and social sciences will advance biogeochemistry through (1) better incorporation of mechanisms, including contemporary evolutionary adaptation, to predict changing biogeochemical cycles, and (2) implementing new and developing insights from social sciences to better understand how sustainable and equitable responses by society are achieved. The challenges for biogeochemists in the 21st century are formidable and will require both the capacity to respond fast to pressing issues (e.g., catastrophic weather events and pandemics) and intense collaboration with government officials, the public, and internationally funded programs. Keys to success will be the degree to which biogeochemistry can make biogeochemical knowledge more available to policy makers and educators about predicting future changes in the biosphere, on timescales from seasons to centuries, in response to climate change and other anthropogenic impacts. Biogeochemistry also has a place in facilitating sustainable and equitable responses by society.
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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 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".