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Record W16335987 · doi:10.2134/agronmonogr41.c33

Reclamation of Smelter‐Damaged Lands

2000· book-chapter· en· W16335987 on OpenAlex
Keith Winterhalder

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAgronomy monograph/Agronomy · 2000
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicCoal and Its By-products
Canadian institutionsLaurentian University
Fundersnot available
KeywordsLand reclamationTailingsEnvironmental scienceSmeltingArable landNatural (archaeology)EcosystemEnvironmental protectionGeographyEnvironmental engineeringAgricultureEcologyArchaeology

Abstract

fetched live from OpenAlex

This chapter deals with the impact of gaseous and particulate emissions from smelter smokestacks on natural landscapes, and excludes consideration of dumps of solid smelter waste. Approaches to the reclamation of industrially disturbed landscapes vary, from benign neglect at one end of the spectrum to complete ecosystem restoration at the other. The approach taken by the Sudbury, Regional Land Reclamation Program can be thought of as minimal amelioration, since the limestone application rarely raises the soil pH by more than one unit, and neither limestone nor fertilizer treatment is repeated after the first application. The use of covering materials is fairly common in the reclamation of solid wastes such as tailings, but it also is used occasionally on degraded landscapes where they are severely metal-contaminated. The benefits of revegetating a degraded landscape are many, including aesthetic enhancement, the return to a functioning ecosystem, the provision of wildlife habitat, and the provision of recreational amenities.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient 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: Other · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0240.001

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 head0.017
GPT teacher head0.185
Teacher spread0.168 · 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