From sea squirts to squirrelfish: facultative trace element hyperaccumulation in animals
Bibliographic record
Abstract
The hyperaccumulation of trace elements is a widely characterized phenomenon in plants, bacteria, and fungi, but has received little attention in animals. However, there are numerous examples of animals that specifically and facultatively accumulate trace elements in the absence of elevated environmental concentrations. Metal hyperaccumulating animals are usually marine invertebrates, likely owing to environmental (e.g. constant exposure via the water) and physiological (e.g. osmoconforming and reduced integument permeability) factors. However, there are examples of terrestrial animals (insect larvae) and marine vertebrates (e.g. squirrelfish) that accumulate high body and/or tissue metal burdens. This review examines examples of animal hyperaccumulation of the elements arsenic, copper, iron, titanium, vanadium and zinc, describing mechanisms by which accumulation occurs and, where possible, hypothesizing functional roles. Groups such as the ascidians (sea squirts), molluscs (gastropods, bivalves and cephalopods) and polychaete annelids feature prominently as animals with hyperaccumulating capacity. Many of these species are potential model organisms offering insight into fundamental processes underlying metal handling, with relevance to human disease and aquatic metal toxicity, and some offer promise in applied fields such as bioremediation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".