BioGeoSCAPES: Ocean metabolism and nutrient cycles on a changing planet
Bibliographic record
Abstract
Global biogeochemical cycles in which essential elements are transformed and recycled are governed by microbial processes. Despite international efforts of studying these important cycles, fundamental questions remain especially regarding fluxes and regulation. The international BioGeoSCAPES initiative aims to unravel the intricacies of these interconnected biogeochemical cycles and improve our understanding of the microbial biogeochemistry of the oceans from regional to ocean basin-scale on a changing planet. The community envisions a more quantitative and predictive understanding of ocean biogeochemical cycles and metabolism by combining detailed information of nutrient/metabolite fluxes, plankton and biochemical processes. The program has an integrative and multidisciplinary approach, by combining state-of-the-art methods in biochemistry, omics, physiology and modeling. Within the scope of BioGeoSCAPES standardized best practices will be established and intercalibration efforts carried out to create an international interoperable data system that nations around the world can contribute to and participate in.Currently, a globally-supported science plan is being developed, in which key scientific interests are identified such as mapping key metabolisms over space and time, measuring rates to connect microbial metabolisms to biogeochemical cycles, and predicting interactions with environmental change. In the near future, the BioGeoSCAPES community will work towards integrating modeling efforts across a range of scales and to develop the infrastructure to support this global initiative. Initial objectives of the science plan will be presented to discuss with the Ocean Sciences community and to receive feedback.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".