Bibliographic record
Abstract
Observations indicate that the rate of increase in concentration of atmospheric CO 2 is increasing faster than projected in any of the Intergovernmental Panel on Climate Change (IPCC) emission scenarios.Several mitigation measures, referred to as 'geoengineering options', have been proposed to remove CO 2 from the atmosphere.To be successful, such a mitigation operation must remove 'significant' CO 2 from the atmosphere for many decades, be verifiable, and not cause deleterious side effects.One option, purposeful addition of iron to fertilize photosynthetic uptake of CO 2 by phytoplankton in regions of the ocean where iron is a limiting nutrient, has received considerable scientific attention.In the last 15 yr, a dozen small-scale open ocean iron fertilization experiments have been performed and a succession of models of large-scale fertilization have been developed.As successive models have become more realistic, the amounts of CO 2 forecast to be sequestered have dropped, and in all cases are small relative to the amounts of CO 2 projected to be released through fossil fuel burning over the next century for any of the IPCC emission scenarios.Possible side effects include a long term reduction in ocean productivity, alteration of the structure of marine food webs, and a more rapid increase in ocean acidity.Most importantly, increased remineralization associated with the increased downward export of organic carbon particles would result in increased production of the third most important long-lived greenhouse gas, N 2 O.The magnitude of this effect is poorly known.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".