Iron plays a role in nitrate drawdown by phytoplankton in Lake Erie surface waters as observed in lake-wide assessments
Bibliographic record
Abstract
Phytoplankton interactions with iron (Fe) were examined in surface waters of Lake Erie during summer thermal stratification. Lake-wide sampling in June and September 2005 was conducted using a continuous surface water sampler (1 m sampling depth) and in July at 18 hydrographic stations (5 m sampling depth). In situ measurements of photosynthetic efficiency (maximum quantum yield of photosystem II) and phytoplankton community composition were measured using fast repetition rate fluorometry and a phytoplankton pigment-specific fluorometer, respectively, during June and September. High ratios (73%–85%) of intracellular Fe to particulate Fe coincident with increases in chlorophyll a (Chl a) concentrations in the western and central basins in June and July imply that the majority of Fe in these regions was associated with intracellular pools. Correlations between intracellular Fe and Chl a were frequently observed when Heterokontophyta and Pyrrophyta dominated the phytoplankton community. Assimilation of Fe by the phytoplankton strongly influenced its partitioning between the dissolved and particulate phase. Dissolved iron (<0.45 µm) concentrations were proportional to Chl a concentrations and both dissolved iron and Chl a were inversely proportional to nitrate concentrations in July and September, suggesting that dissolved iron influenced both nitrate drawdown and Chl a concentrations in Lake Erie surface waters in summer.
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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".