Improved methods for quantifying soil invertebrates during ecotoxicological tests: Chill comas and anesthetics
Bibliographic record
Abstract
Soil invertebrate ecotoxicological tests are important when making informed site-management decisions. However, traditional tests are time-consuming and require quantification of high numbers of soil invertebrates burrowed beneath the surface of soil. A commonly used technique to extract invertebrates from the soil is the floatation method. Due to the movement of Collembola, and the presence of small soil particulates and bubbles on the surface of the water, automatic image analysis software may inaccurately quantify the true number of individuals present. Hence, manual counting immediately following extraction, or from images, is still the most effective method utilized for quantifying floated soil invertebrates. This study investigated three novel techniques; the use of an ice-water bath, chest freezer (−12 °C) and ethanol to temporarily immobilize groups of 35 Folsomia candida individuals to increase accuracy during the quantification step. Active thermography to aid automatic image analysis was also investigated. Results show that while thermoimaging did not provide a distinct advantage in differentiating soil invertebrates from soil particles, both an ice-water bath and 4.75% ethanol solution were extremely effective at temporarily immobilizing F. candida with no apparent ill effects. The outcome of this study will assist future soil invertebrate research by increasing the accuracy of invertebrate quantifications. In addition, as the techniques caused no mortality to the invertebrates, the same individuals remain available for continuous monitoring experiments, repeated exposure, and/or multi-generational studies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".