Chitobiase as a surrogate measure of aquatic invertebrate biomass and secondary production in an environmental effects monitoring context
Bibliographic record
Abstract
The current techniques used to assess aquatic invertebrate community status in the field are typically labour and time intensive, and therefore the development and implementation of new rapid and cost-effective methodologies is warranted. A proposed option is the enzymatic assay to detect and quantify the rate of production of the molting enzyme chitobiase, which can be used for determining impacts on freshwater aquatic systems. Two case studies were performed at: 1) The Prairie Wetland Research Facility at the University of Manitoba, to determine if a relation exists between measures of chitobiase and aquatic invertebrate biomass in a mesocosm setting, as well as to determine if changes in chitobiase activity could detect impacts to aquatic invertebrate communities from sulfamethoxazole and diluted bitumen and; 2) in the Elk River Valley region of British Columbia, to determine if a positive relationship exists between the rate of chitobiase production and benthic invertebrate biomass in lotic freshwater systems. No significant relationship was observed between the chitobiase measures and invertebrate biomass measures, and no effects of the stressors were detected in the first study. A significant positive relationship was observed between the rate of chitobiase production and benthic invertebrate biomass in the second study. It is recommended that additional studies be performed to further assess the potential of chitobiase activity to be used in an environmental monitoring context.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".