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Record W7100012588

ECOSYSTEM RECOVERY AFTER EMISS ION REDUCTIONS:

2016· article· en· W7100012588 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicCultural and Sociopolitical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystemAquatic ecosystemTerrestrial ecosystemVegetation (pathology)MicroclimateLichenInvertebrateParticulates
DOInot available

Abstract

fetched live from OpenAlex

Abstract. A case history is presented escribing the ecosystem changes that accompanied the nearly 90 % reduction of SO2 and metal particulate emissions from Sudbury smelters during the past 25 years. The instances of severe ground-level fumigations that caused acute damage to vegetation i an area of approximately 1,000 km 2 have been nearly completely eliminated. Significant improvements in water quality have also occurred in many of the estimated 7,000 acid-damaged lakes. Several species of acid-sensitive phytoplankton, zooplankton and insects have invaded lakes where improvements have occurred. Epiphytic lichens have reinvaded the former "lichen desert " that once extended out 7 km from the smelters. Sensitive species uch as Evernia mesomorpha and Usnea hirta now exist throughout the area. The vascular plant communities have been relatively slow to recover in the most severely damaged terrestrial reas. Metal-tolerant grasses (e.g. Agro~tis cabra, Deschampsia c espitosa) were the first species to invade the barrens. Acid- and metal-contamination of soil, severe microclimate conditions, and the damaging effects of insect pests appear to delay recovery of terrestrial ecosystems. Recovery rates of aquatic ecosystems are also affected by a suite of physical, chemical and biotic interactions and many lakes remain severely damaged.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score1.000

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.031
GPT teacher head0.209
Teacher spread0.178 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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