Bibliographic record
Abstract
Insects encounter a variety of metals in their environment, many of which are required at some concentration for normal organismal homeostasis, but essentially all of which are toxic at higher concentrations. Insects have evolved a variety of genetic, and likely epigenetic, mechanisms to deal with metal stress. A recurring theme in all these systems is complexity and diversity; even simple, single gene, cases are complex. Of the known gene families, the metallothioneins are perhaps the best understood and provide good examples of how diverse metal response is. Interestingly, there is considerable diversity across taxa in these metal-responsive systems, including duplications to form small gene families and complex expression of single loci. Strikingly, different species have evolved different mechanisms to cope with the same, or similar, stress suggesting both independent derivation of, and plasticity in, the pathways involved. It is likely that some metal-response systems evolved early in evolutionary time and have been conserved, while others have diverged, and still others evolved more recently and convergently. In addition to conventional genetics, insects likely respond to environmental metal through a variety of epigenetic systems, but direct tests are lacking. Ultimately, it is likely that classical genetic and epigenetic factors interact in regulating insect metal responses. In light of this diversity across species, future studies including a broad-based examination of gene expression in non-model species in complex environments will likely uncover additional genes and genetic and epigenetic mechanisms.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".