An investigation into iron-stimulated phytoplankton productivity in epipelagic Lake Erie during thermal stratification using trace metal clean techniques
Bibliographic record
Abstract
We tested the hypothesis that phytoplankton productivity in pelagic Lake Erie is limited by low Fe bioavailability during the period of thermal stratification. Iron enrichment (20 and 200 nM Fe) of water sampled from the eastern basin surface water (5 m depth) in July 1996 revealed a dramatic 180 and 30% increase in the standing crop of the picoplankton (0.2-2 µm) and nanoplankton (2-20 µm) size fractions, respectively. Light-saturated rates of photosynthesis for picoplankton increased 2.8 times that of the controls within 24 h. In contrast, simultaneous P and Fe enrichment experiments carried out in July 1997 and August 1998 with water samples from three pelagic stations revealed that P enrichment alone stimulated phytoplankton growth, although at several stations, phytoplankton yield was greater in combined P- and Fe-amended experiments relative to P-enriched treatments. Periodogram analysis of meteorological buoy wave height data and heat budgets calculated from thermal profiles in the eastern basin revealed important interyear differences in precruise conditioning of the epilimnion. Low-frequency, turbulent mixing events that characterized the 30-day precruise period in the years 1997 and 1998 relative to 1996 may have prevented Fe-stressed conditions in the phytoplankton community studied in 1997 and 1998.
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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 | 0.000 | 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) | 0.000 | 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".