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Record W2245380718 · doi:10.2166/wqrj.2010.015

Monitoring of the Environmental Effects of Pulp Mill Discharges in Chilean Rivers: Lessons Learned and Challenges

2010· article· en· W2245380718 on OpenAlexafffund
Gustavo Chiang, Kelly R. Munkittrick, Rodrigo Orrego, Ricardo Barra

Bibliographic record

VenueWater Quality Research Journal · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsOntario Tech UniversityUniversity of New Brunswick
FundersComisión Nacional de Investigación Científica y TecnológicaMcMaster University
KeywordsPaper millEnvironmental scienceSpecies richnessPulp millMillStandardizationPulp (tooth)EffluentEcologyEnvironmental protectionGeographyBiologyEnvironmental engineeringComputer science

Abstract

fetched live from OpenAlex

Abstract Environmental monitoring activities in Chile are relatively new and have traditionally relied on physicochemical measurements. The pulp mill industry in Chile is highly competitive in the global market and several new large mills have recently opened. Early studies on fish in the receiving environments revealed lower species richness and an increase in the abundance of introduced species relative to native ones near pulp mill discharges. Even though changes were observed, their relationship with the discharges was unclear. Several difficulties related to small body sizes and the unavailability of basic biological data for native Chilean fish species were found during initial field studies. One of the main challenges is the standardization of monitoring methods (including fish species selection, sampling sizes, indicators, reference sites, etc.) and consensus about the responses that should be considered in a river monitoring program in the Chilean context. This paper summarizes major findings from a series of studies looking at impacts on fish at different levels of biological organization and the current approach used in Chile for monitoring impacts of pulp mill effluents on wild fish populations.

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 score0.052
Threshold uncertainty score0.332

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.108
GPT teacher head0.361
Teacher spread0.253 · 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

Citations17
Published2010
Admission routes2
Has abstractyes

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