Evidence of interregional similarity in crayfish metabolomes at reference sites: Progress towards the metabolome as a biomonitoring tool
Bibliographic record
Abstract
It has been proposed that biomonitoring may benefit from the use of metabolomics (the study of all small molecules in an organism) to detect sub-lethal organism stress through changes in the metabolite profile (i.e., the metabolome). However, to integrate the metabolome into biomonitoring programs the amount of natural variability among and within populations of indicator taxa must be established prior to generating a reference condition. This study determined variation in the metabolome among ecoregion and stream of origin in the northern crayfish (Faxonius virilis) and if that variation inhibited detection of stressor effects at sites exposed to human activities. We collected crayfish from seven minimally disturbed streams (i.e., reference streams), distributed across three level II ecoregions in central Canada and compared their metabolomes. We found ecoregion and stream origin were poor predictors of crayfish metabolomes. This result suggests crayfish metabolomes were similar, despite differing environmental conditions. Metabolomes of crayfish collected from three stream sites exposed to agricultural activity and municipal wastewater (i.e., test sites) were then compared to the crayfish metabolomes from the seven reference streams. Findings showed that crayfish metabolomes from test sites were strongly differentiated from those at all reference sites. The consistency in the northern crayfish metabolome at the studied reference streams indicates that a single reference condition may effectively detect impacts of human activities across the sampled ecoregions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".