Bibliographic record
Abstract
Oceanographers and atmospheric scientists were pretty sure that the oceans must have taken up most of the missing CO/sub 2/, but it is only with the completion of the latest study that this belief is confirmed by solid empirical evidence. The major findings are, first, that nearly half of the carbon dioxide that humans have pumped into the atmosphere over the last 200 years has been absorbed by the oceans, and, second, that the rising CO/sub 2/ concentrations could start to have empirically filling a link in the general model of where CO/sub 2/ originates and where it ends up, is that between 1800 and 1994, the ocean absorbed 48 percent of total CO/sub 2/ emissions serious adverse effects on some marine life. In the case of the oceans, increasing the CO/sub 2/concentration boosts acidity, which is neutralized when CO/sub 2/reacts with calcium carbonate and water to form ions of HCO/sub 3/ and calcium. And the reverse is true as well, with the ions reacting to form CO/sub 2/, water, and calcium carbonate. In the face of increased acidity from rising CO/sub 2/levels, the ocean tries to maintain its equilibrium by increasing the rate of reaction in the direction of making more ions. But that means consuming more calcium carbonate, which unfortunately for ocean life is the stuff of bone, shell, and coral.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".