MétaCan
Menu
Back to cohort
Record W2073495282 · doi:10.5558/tfc79075-1

1998 ice storm impact on sugar bushes of eastern Ontario

2003· article· en· W2073495282 on OpenAlexaffvenueabout
Thomas L. Noland

Bibliographic record

VenueThe Forestry Chronicle · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsOntario Forest Research Institute
Fundersnot available
KeywordsSweetnessSugarStarchHorticultureChemistryCrown (dentistry)LimeAgronomyBiologyBotanyFood scienceMedicine

Abstract

fetched live from OpenAlex

In 1998, thirty-four managed sugar bush blocks with 7% to 72% ice-induced crown damage were established in eastern Ontario. All received dolomitic lime (Ca, Mg) and P and K treatments in June 1999. Initial crown damage, fall root starch and sugar levels, sap production and sweetness, sap amino acid concentration, and tap hole closure rate were measured. Syrup production was calculated. Trees with >50% (severe) crown damage had reduced root starch content in 1998 and 2000, but not in 1999. In 2000, root total sugars were lower in trees with >50% crown damage. Sap produced per tap, and sap sweetness were reduced by damage, but not consistently in all years. Sap per tap increased with total crown branches in all three years, but sap sweetness and syrup production only in 1999. Syrup production per tap was consistently reduced in damaged trees in all three years, usually in trees with > 50% damage. The lime and P and K treatments did not significantly affect any of the variables measured. Results suggest that severe ice storm damage to crowns resulted in reduced fall root starch levels and less sap production, and/or sap sweetness, and therefore lowered the syrup producing capacity of sugar maple. Key words: Acer saccharum, ice storm, sap and syrup production, root starch, crown damage

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.568

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.217
Teacher spread0.192 · 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

Citations5
Published2003
Admission routes3
Has abstractyes

Explore more

Same venueThe Forestry ChronicleSame topicPlant Surface Properties and TreatmentsFrench-language works237,207