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Record W4323051270 · doi:10.1016/j.foreco.2023.120896

TAMM review: On the importance of tap and tree characteristics in maple sugaring

2023· article· en· W4323051270 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueForest Ecology and Management · 2023
Typearticle
Languageen
FieldChemistry
TopicPlant-Derived Bioactive Compounds
Canadian institutionsUniversité TÉLUQUniversité du Québec à ChicoutimiUniversité du Québec à MontréalUniversité du Québec en Outaouais
FundersNatural Sciences and Engineering Research Council of CanadaMinistère des Forêts, de la Faune et des ParcsU.S. Geological SurveyUniversité du Québec à ChicoutimiNational Science Foundation
KeywordsMapleSugarTap waterYield (engineering)BiologyHorticultureBotanyAgronomyEnvironmental scienceFood science

Abstract

fetched live from OpenAlex

Maple sugaring mainly uses sugar and red maples (Acer saccharum and Acer rubrum) by tapping them for sap in the leafless-state across large portions of their ranges. How much sap exudes from a tap hole and how sweet this sap is, can vary substantially. Year-to-year variation in sap yield and sugar content can be primarily traced to differences in meteorological conditions that drive sap runs. Yet, how much of the total variation in sap yield and sugar content is linked to the year, site, species, tree, or tap has not been investigated systematically. Here, we reviewed the literature and also compiled a dataset of sap yield and sugar content from gravity taps on 324 red and sugar maples. The compiled data originates from multiple studies at ten sites across a large proportion of the ranges of sugar and red maple and stretches over eleven years. Using about 15 000 data points on sap yield and sap sugar content, we analysed the importance of tap and tree characteristics, such as height of the tap hole on the stem or diameter at breast height. We also review previous research on the importance of tap and tree characteristics in maple sugaring. Moreover, we partition variability in the data to attribute it to species, site, tree, year, and tap characteristics. Our results indicate that species, site and tree characteristics are the three largest sources of variability with regards to sap yield and the sap’s sucrose concentration. However, differences between years and tap characteristics, which were found to be comparatively minor sources of variability in sap yield and the sap’s sucrose concentration, have attracted far more attention in the past. We advocate for the continuation and expansion of systematic measurements of sap characteristics across a network of sites to further improve our understanding of maple sugaring. Such an understanding will be instrumental to prepare maple sugaring operations against the imminent effects of the climate and biodiversity crises and ensure their sustainability to perpetuate this traditional activity.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.255

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 studies0.0000.000
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 head0.016
GPT teacher head0.235
Teacher spread0.218 · 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