HISTORICAL METAL CONCENTRATIONS IN LACUSTRINE FOOD WEBS REVEALED USING FOSSIL EPHIPPIA FROM<i>DAPHNIA</i>
Bibliographic record
Abstract
Metal contamination of freshwater ecosystems is increasingly prevalent due to anthropogenic activities such as metal smelting and fossil fuel combustion. While toxicological studies focus on aqueous metal concentrations that result in lethal or sublethal responses, currently the only method for reconstructing a lake's metal contamination history is through an examination of the sedimentary deposits. In this paper, we suggest that cladoceran diapausing eggs (ephippia), which are abundant in nature and accumulate maternally derived metals, can be used to measure historical variations in biologically relevant metals that derive from the water column (water, diet). Linear regressions of total metal content against ephippia density or mass were strong (R2 > 0.80, P < 0.04) and revealed that metals were incorporated into ephippia with little contamination from the sediment matrix. Comparison of metal concentrations in ephippia and bulk sediments from three lakes demonstrated that some metals associated with urban sources (Cd, Cr, Mo) were preferentially concentrated in ephippia, whereas concentrations of other metals indicating landscape erosion (Al, Ca, Fe, Mn) exhibited greater concentrations in bulk sediments than in diapausing eggs. Because historical changes in metals within fossils and bulk sediments were uncorrelated in most instances, past variation in the metal content of ephippia provided a unique history of food web exposure to metals in the water column.
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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 | 0.001 | 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".