MétaCan
Menu
Back to cohort
Record W2884546284 · doi:10.5772/intechopen.74112

Understanding Perturbation in Aquatic Insect Communities under Multiple Stressor Threat

2018· book-chapter· en· W2884546284 on OpenAlexfundno aff
Alexa C. Alexander

Bibliographic record

VenueInTech eBooks · 2018
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change Canada
KeywordsMesocosmStressorEcologyAquatic insectCumulative effectsEcosystemBiologyEnvironmental resource managementEnvironmental scienceHabitat

Abstract

fetched live from OpenAlex

In the scientific literature, there is a considerable consensus that working toward evaluating multiple stressors is worthwhile. Unfortunately, our means to evaluate the combined effects of multiple stressors on species is limited. In agricultural systems, the relative threat posed to aquatic insect communities due to individual stressors (e.g., individual insecticides) is relatively well understood. However, understanding mixtures of pesticides, let alone the addition of complex and potentially interacting, natural gradients (e.g., nutrients and predation), is far harder. The objective of the following review was to evaluate the individual and combined effects of a range of multiple agricultural stressors on aquatic insect communities using a series of seven outdoor mesocosm experiments conducted since 2003. The mesocosm studies show that macroinvertebrate community responses can be similar, subtle, or even opposing depending on the stressors investigated and the mechanistic or ecological focus of the study. The current focus on individual chemicals and responses to treatment is misleading. Cumulative effects and multiple sublethal stressors are the norm in impacted ecosystems. A simple, holistic approach to environmental risk assessment is needed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.269
GPT teacher head0.288
Teacher spread0.019 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInTech eBooksSame topicInsect and Pesticide ResearchFrench-language works237,207