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Record W2135887414 · doi:10.1139/b08-051

Soluble carbohydrate content of shoots of Arctic wetland plants that are consumed by lesser snow geese

2008· article· en· W2135887414 on OpenAlexafffundvenueabout
Robert L. Jefferies, Kate A. Edwards

Bibliographic record

VenueBotany · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaChurchill Northern Studies Centre
KeywordsBiologyShootForageSnowBayBotanyArctic vegetationGrowing seasonForagingSpartina alternifloraArcticAgronomyEcologyWetlandTundra

Abstract

fetched live from OpenAlex

We recorded seasonal changes in the total amounts of soluble carbohydrates in shoots of salt- and fresh-water coastal plants at La Pérouse Bay, northern Manitoba, to determine whether adult snow geese and their goslings selected forage rich in soluble carbohydrates during the breeding season. The selection of forage plants in spring and summer by adults and goslings was strongly linked to the presence of high amounts of soluble carbohydrates in tissues: on the order of 100 mg·g–1dry mass. When the content fell as a result of shoot development or leaf senescence, the geese switched to alternative sources of forage. The extent to which individual shoots rich in soluble carbohydrates of the primary freshwater forage species are grazed depends on the local density of breeding geese at the study site, which has fallen in the last decade as a result of the earlier destruction of much of the coastal vegetation by foraging geese.

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 score0.018
Threshold uncertainty score0.036

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 studies0.0000.000
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 head0.065
GPT teacher head0.229
Teacher spread0.165 · 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
Published2008
Admission routes4
Has abstractyes

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