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Record W2510884839

Recommended Liming Strategies for Salmon Rivers in Nova Scotia, Canada

2001· article· en· W2510884839 on OpenAlexaboutno aff
Atle Hindar

Bibliographic record

VenueBIBSYS Brage (BIBSYS (Norway)) · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaNova (rocket)FisheryGeographyEnvironmental scienceForestryArchaeologyBiologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

The Southern Upland of Nova Scotia is acidified due the extreme sensitive ecosystems combined with being down-wind from large sulphur emission sites in southern Canada and northern US. Atlantic Salmon populations have disappeared from 14 rivers and populations have been damaged in another 35 rivers. Only rivers with pH>5.4 have non-damaged populations. Attempts to improve this situation have been restricted to the genereal emission control plans of Canada. No liming program exists at present. This report gives detailed information regarding liming strategies in general and for four major river systems in particular. East and West River, Sheet Harbour, together with La Have and Medway River are regarded as being typical representatives of the damaged rivers. Liming of these rivers is possible, and a combination of lake liming, river lime dosing and catchment liming is recommended. Suggestions are also made regarding the approach towards a relatively high-quality liming strategy for the province, and the establishment of a monitoring program for water chemistry and biology.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.229
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2001
Admission routes1
Has abstractno

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