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Record W2001651515 · doi:10.5558/tfc76783-5

The Ontario Hardwood Forest Health Survey: 1986 – 1998

2000· article· en· W2001651515 on OpenAlexaffvenueabout
D. L. McLaughlin, MH Chiu, D. Durigon, H. Liljalehto

Bibliographic record

VenueThe Forestry Chronicle · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsMinistry of Natural Resources and ForestryMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsSoil waterHardwoodTree healthEnvironmental scienceForestryGeographyForest healthPollutionAcid depositionEnvironmental protectionAgroforestryEcologyBiologySoil science

Abstract

fetched live from OpenAlex

In 1986 the Ontario government established a long-term program to monitor hardwood forest health. The program was initiated in response to widespread reports of forest decline in North America and Europe and the implication that air pollution, specifically acidic deposition, was a causal factor. Visual symptoms of branch dieback, leaf colour, and leaf size, have been quantitatively evaluated on about 15 000 trees in 110 plots. This report summarizes the results of the first 12 years (1986 to 1998). Over that time period, relative to the 1986 baseline year, 84% of the plots have improved in condition, 12% have not changed, and 4% have deteriorated. Generally, hardwood forest health in the province appears to be quite good; severe decline is limited and very site-specific, occurring only in selected northern regions on acid-sensitive and/or marginal sites, or in southern areas on very shallow soils. Northern forests growing on coarse-textured shallow soils underlain by precambrian rock are in poorer health relative to southern forests growing on finer-textured, deeper soil over limestone. On soils sensitive to acidic deposition, tree health deteriorated as soil pH and exchangeable aluminum levels increased. Key words: forest health, forest decline, decline index, Ontario, hardwood, air pollution, acid rain

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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.

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

Citations18
Published2000
Admission routes3
Has abstractyes

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