The Global Iron Cycle
Bibliographic record
Abstract
It should come as no surprise that iron, the fourth most abundant element in the Earth’s crust (Taylor and McLennan, 1985), is essential in biology. Yet, in today’s oceans, iron is a vanishingly rare element (Fig. 6.1). Its concentration – typically <1 nM (Johnson et al., 1997; Boye et al., 2001; Cullen et al., 2006) – is so low that iron scarcity limits biological productivity across large areas of the Earth’s surface (Martin and Fitzwater, 1988). This peculiar situation is a consequence of the chemical behaviour of iron on an oxygenated Earth. In the pres-ence of abundant O2, the element is found primarily in the Fe(III) oxidation state, which forms poorly soluble oxyhydroxides. Why, then, is iron required by biology? Most likely, this is a legacy of early evolution when iron was ubiquitous on land and in the sea. It also helped
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.184 | 0.004 |
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; both teacher heads agree on what is shown here.
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".