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Record W2310033568 · doi:10.2166/wqrj.2002.004

Sublethal Toxicity Findings by the Pulp and Paper Industry for Cycles 1 and 2 of the Environmental Effects Monitoring Program

2002· article· en· W2310033568 on OpenAlexaffabout
Richard P. Scroggins, Jennifer A. Miller, Anne I. Borgmann, John B. Sprague

Bibliographic record

VenueWater Quality Research Journal · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsEffluentToxicityPaper millPulp (tooth)ToxicologyEnvironmental sciencePulp millBiologyChemistryEnvironmental engineeringMedicine

Abstract

fetched live from OpenAlex

Abstract Sublethal toxicity tests successfully measured the improved quality of pulp mill effluents from the first cycle of environmental effects monitoring (1992–1996) to the second cycle (1997–2000). Test endpoints showed notable shifts to higher concentrations (less toxic). During the second cycle of monitoring, significantly more tests showed no effect in full-strength effluent. Five case studies were considered as part of this exercise. Most of the improvement came with installation of secondary treatment. Twelve Ontario mills with secondary treatment showed reduced toxicity, compared to results with primary treatment. All 29 sets of sublethal data showed higher IC25s during the second cycle, and 23 of these differences were statistically significant. Any other changes between the two cycles of study caused only marginal overall improvement in toxicity, judging by 12 freshwater mills in British Columbia which had secondary treatment during both cycles. Sublethal tests successfully predicted the zone of potential effect in receiving water, agreeing with effects observed in biological surveys. Overlapping zones from multiple discharges could also be demonstrated. In a situation near Niagara Falls, sublethal tests estimated the proportions of toxic loading that four mills contributed to one water body. The prediction was realistic; the actual toxicity found for a mixed effluent was 57% of that predicted from separate toxicities. The conservative prediction agrees with the usual less-than-additive sublethal action of combined toxicants.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.070
GPT teacher head0.354
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2002
Admission routes2
Has abstractyes

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