Toxicity and chemistry of aspen wood leachate to aquatic life: Field study
Bibliographic record
Abstract
A dark, toxic leachate has been observed around woodpiles of trembling aspen (Populus tremuloides Michx.) cut in winter for pulp or structural lumber. We measured production of leachate from 18 m3 of harvestable aspen logs stacked in an open field near Dawson Creek, British Columbia, Canada. The logpile began producing leachate during the first winter thaw and continued to do so for the duration of the two-year study (mean, 250 L/collection). Aspen leachate was characterized by dark color, acidic pH (5.0-6.5), elevated conductivity (200-500 microS/cm), high to very high biochemical oxygen demand (500-5,000 mg/L) and total organic carbon concentrations (500-2,000 mg/L), variable levels of phenolic compounds (2-27 mg/L), and low dissolved oxygen tensions (<2 mg/L). In tests with rainbow trout (Oncorhynchus mykiss), Daphnia magna, and luminescent bacteria, the leachate varied from weakly toxic (median lethal concentration, >10%) to very toxic (median lethal concentration, <1%). The volume of leachate generated by the logpile was correlated with total precipitation (rain or snow) since the last collection. Loads of chemical constituents or toxicity (lethal concentration x volume) in the leachate did not decline over the duration of the study. Less than 10% of the total mass of leachable material in the aspen logs was removed during two years of exposure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".