Bibliographic record
Abstract
This study explored Hg bioaccumulation with age in three Lake Huron lake trout populations, considering effects of growth and trophic dynamics. Hg concentrations and stable isotopes were measured in trout, smelt, round goby, zooplankton and zebra mussels. Trout populations demonstrated exponentially increasing Hg concentrations with age and revealed basin-specific accumulation patterns. High biomagnification Factor (BMF) correlated with low prey densities suggest that physiological and ecological factors regulating fish growth rates such as foraging efficiencies are important in regulating Hg bioaccumulation. Physiological processes affect Hg bioaccumulation, specifically elimination dynamics. Hg in liver, gonads, dorsal muscle, and remaining carcass in pre-spawn, spawning, and post-spawn yellow perch populations were investigated. Ratio of Hg in each tissue to whole-body Hg were different between male and female perch, as well as among pre-, during-, and post-spawning perch. Thus, changes in Hg tissue concentrations during spawning could result in high variability of Hg elimination rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".