The effect of vertical and horizontal dilution on fertilized patch experiments
Bibliographic record
Abstract
A great deal of attention, both negative and positive, has been directed at the potential of large‐scale iron fertilization schemes to sequester carbon by inducing phytoplankton blooms that would, in theory, result in significant export of organic carbon to the deep ocean in high nitrogen ‐ low chlorophyll regions. A suite of iron manipulation or ‘patch’ experiments has been performed over length‐scales of 10s of km. Here, we use a physical‐ecological‐chemical model, with prognostic nitrogen, silica and iron dynamics, to study one of the most successful of these experiments, the Subarctic Ecosystem Response to Iron Enrichment Study (SERIES), focusing on the vertical export of organic material, which is difficult to observe in the field. The implications of large‐scale fertilization, i.e. increasing patch size, are investigated. Our results agree with the general conclusions obtained from the field experiments. Only a modest export of organic carbon occurs (less than 25% of carbon uptake by phytoplankton) at the base of the mixed layer. Furthermore, we show that lateral and vertical supply of silicic acid is necessary to fuel a sustained phytoplankton bloom. Increasing patch size results in less lateral nutrient supply relative to patch area and so a decrease, not only in total production (per unit area), but in the contribution by large phytoplankton due to silica limitation. Most importantly, the export of organic carbon (per unit area) decreases substantially, by nearly an order of magnitude, as scales of 1000 km are approached.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".