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Record W6964641696 · doi:10.25921/st7f-9e23

NOAA/WDS Paleoclimatology - Copenheaver - Entriken - ACSH - ITRDB PA019

2022· dataset· en· W6964641696 on OpenAlexaboutno aff

Bibliographic record

VenueNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) · 2022
Typedataset
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsnot available
Fundersnot available
KeywordsMapleCambiumSugarTree (set theory)Production (economics)Liberian dollarReduction (mathematics)

Abstract

fetched live from OpenAlex

The conversion of sap from sugar maple (Acer saccharum) to syrup is a multi-million dollar agroforestry industry in North America. Sugar maple trees take decades to reach a size that can be tapped; therefore, maintaining vigorous trees is crucial to the industry's sustainability. Our objectives were to identify whether syrup production altered radial growth or cambium miner activity. We extracted increment cores and measured ring widths from trees tapped for syrup production and untapped reference trees. In Pennsylvania and Ontario, radial growth in tapped trees was significantly reduced compared to reference trees. In New York, there was no significant difference in radial growth between tapped and reference trees. All sites showed a significant reduction in radial growth following commencement of tapping, compared to growth rates prior to tapping. This pattern was not identified in reference trees, which indicates that it was not an artifact of age-related growth trends. There was no significant difference between cambium miner activity in tapped and reference trees. We concluded that the reduction in sugar maple radial growth is likely due to reallocation of resources as the tree heals the damage to the stem during tapping.

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)0.004
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.072
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0720.091

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI)Same topicEcology and biodiversity studiesFrench-language works237,207