EVOLUTION OF AN ARTIFICIAL SEAWATER MEDIUM: IMPROVEMENTS IN ENRICHED SEAWATER, ARTIFICIAL WATER OVER THE LAST TWO DECADES
Bibliographic record
Abstract
Although most phycologists use natural seawater for culturing marine species, artificial media continue to play important roles in overcoming problems of supply and seasonal variability in the quality of natural seawater and also for experiments involving manipulation of micro‐ and macronutrients. Several artificial media have been developed over the last 90 years; enriched seawater, artificial water (ESAW) is among the more popular recipes. ESAW has the advantage of an ionic balance that is somewhat closer to that of normal seawater. The original paper compared the growth of 83 strains of microalgae in natural seawater (ESNW) versus ESAW and determined that 23% grew more poorly in the artificial water. Since 1980, however, the composition of ESAW, as used by the original authors, has changed considerably. In particular, the added forms of phosphate, iron, and silicate have been changed and the trace metal mixture has been altered to include nickel, molybdenum, and selenium. We tested whether these changes improved the ability of the artificial medium to grow previously difficult to grow phytoplankton species. To test this, we selected eight species that had been shown to grow better in ESNW than in ESAW and compared their growth again, using the currently used recipe with all the above modifications. For all but one species ( Apedinella spinifera ), growth rate and final yield was no different between the media but in one case ( Emiliania huxleyi ) was slightly higher in ESAW. No differences in cell morphology or volume were found in any case. We conclude that changes to the enrichment portion of the recipe have significantly improved this artificial seawater medium and that it can be used to grow an even wider range of coastal and open ocean species.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".