Increased sensitivity and variability of phytotoxicity responses in Arctic soils to a reference toxicant, boric acid
Bibliographic record
Abstract
Industrial and human activities in the Arctic regions may pose a risk to terrestrial Arctic ecosystem functions. One of the most common terrestrial toxicological end points, primary productivity, typically is assessed using a plant phytotoxicity test. Because of cryoturbation, a soil mixing process common in polar regions, we hypothesized that phytotoxicity test results in Arctic soils would be highly variable compared to other terrestrial ecosystems. The variability associated with phytotoxicity tests was evaluated using Environment Canada's standardized plant toxicity test in three cryoturbated soils from Canada's Arctic exposed to a reference toxicant, boric acid. Northern wheatgrass (Elymus lanceolatus) not only was more sensitive to toxicants in Arctic soils, its response to toxicants was more variable compared to that in temperate soils. The phytotoxicity of boric acid in cryosols was much greater than commonly reported in other soils, with a boric acid concentration of less than 150 microg/g soil needed to inhibit root and shoot growth by 20%. Large variability also was found in the phytotoxicity test results, with coefficients of variation for 10 samples ranging from 160 to 79%. The increased toxicity of boric acid in cryosols and variability in test response was not explained by soil properties. Based on our admittedly limited data set of three different Arctic soils, we recommend that more than 30 samples be taken from each control and potentially impacted area to accurately assess contaminant effects at sites in northern Canada. Such intensive sampling will insure that false-negative results for toxicant impacts in Arctic soils are minimized.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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 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".