MétaCan
Menu
Back to cohort
Record W3138462296 · doi:10.33584/rps.10.2003.2983

Edible forage yield and nutritive value of poplar and willow

2003· article· en· W3138462296 on OpenAlexfundno aff
Peter Kemp, T.N. Barry, Grant Douglas

Bibliographic record

VenueNZGA Research and Practice Series · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
FundersAgricultural and Marketing Research and Development TrustGreater Wellington Regional CouncilMassey UniversityMcGill University
KeywordsWillowForagePastureAgronomyDry matterLivestockYield (engineering)BiologyBotanyEcology

Abstract

fetched live from OpenAlex

Poplar and willow on farms are a potential source of supplementary forage during summer. To incorporate poplar and willow into farm feed budgets, a method is needed to non-destructively estimate the edible forage yield of the trees. Also needed is an estimate of the nutritive value of the forage. Previously uncut trees on hill farms in the lower North Island were measured and a relationship between tree forage yield and diameter of the trunk at breast height (DBH, 1.4 m) was developed. The DBH was from 5 to 32 cm and the forage yield from 1 to 66 kg dry matter (DM)/tree. Nutritive value of poplar and willow (metabolisable energy 8-9 MJ/kg DM) was similar to that of normal summer pasture, but was lower in fibre and higher in soluble carbohydrate, and of higher nutritive value than drought pasture. The concentrations of the secondary chemicals condensed tannins and phenolic glycosides were high in poplars and willows, and they have some positive effects on livestock performance, but their role requires further research. It was concluded that poplar and willow provide forage of sufficient quantity and quality to warrant using them as supplements to pasture for feeding to livestock during summer droughts.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.081
GPT teacher head0.339
Teacher spread0.258 · 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 designBench or experimental
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

Citations17
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueNZGA Research and Practice SeriesSame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207