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Record W29256839 · doi:10.1079/9780851993768.0087

Landscape degradation by smelter emissions near Sudbury, Canada, and subsequent amelioration and restoration.

2000· book-chapter· en· W29256839 on OpenAlexaboutno aff
Keith Winterhalder

Bibliographic record

VenueCABI eBooks · 2000
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsRevegetationWoodlandFumigationEnvironmental scienceForestryAgronomyEcologyGeographyBiologyEcological succession

Abstract

fetched live from OpenAlex

Abstract A century of sulfur dioxide fumigation, copper and nickel particulate deposition, fire, soil erosion and enhanced frost action resulting from mining and smeltering has created 17, 00 ha of barren land and 72 000 ha of stunted, open birch-maple (Betula/Acer) woodland in the Sudbury area of Ontario, Canada. The primary factor limiting plant colonization is the acidic, aluminium-, copper- and nickel-toxic properties of the soils, although certain plant species have developed genetically based metal tolerance. In the revegetation programme, manual surface application of ground limestone, with or without an accompanying fertilizer and/or grass-legume seed application, leads to immediate colonization by woody species including birch (Betula spp.), aspen (Populus tremuloides) and willows (Salix spp.), and more than 3000 ha have been treated in this way by the Regional Municipality of Sudbury since 1978. Native coniferous species (Pinus spp.) have also been planted in groups to form a seed source for future colonization.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.016
GPT teacher head0.180
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2000
Admission routes1
Has abstractyes

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