Effects of oil and bioremediation on mussel ( <i>Mytilus edulis L.</i> ) growth in mudflats
Bibliographic record
Abstract
Mussels (Mytilus edulis L.) were exposed to crude oil during a field experiment to evaluate two bioremediation strategies (nutrient addition and nutrient addition with tilling). The mussels were placed in 4 mesocosms: Control, Oil, Oil + Nutrients, and Oil + Nutrients + Tilled. Tilling appeared to be clearly detrimental to mussel growth. Additionally, this field experiment demonstrated that at temperatures below 5 degrees C, growth was reduced to rates undetectable by the laser diffraction method. The data on mussel shell length show that this technique does offer very sensitive and useful comparative measurements of physiological function. Measurement of shell growth has the advantage over other techniques in that it is non-invasive and non-destructive and thus may be used continuously without disturbing critical physiological and biochemical functions; however, bivalve physiology is strongly linked to environmental conditions, so it is important to include such measures (i.e. seawater temperature and turbidity) in the design of the biomonitoring program. Elevated polycyclic aromatic hydrocarbon (PAH) levels reflected bioaccumulation in mussels from all the oiled mesocosms. This correlated with reduction in growth rate. Maximum reduction in growth was observed in mussels from the tilled mesocosm which contained the lowest phenanthrene and dibenzothiophene concentrations. The tilling caused an increase in suspended solids which inhibited filter feeding activity, and resulted in suppressed growth and slower intake of PAH-laden sediment.
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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) | 0.001 | 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".