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Record W2781872740 · doi:10.3733/ucanr.8345

Youth Development through Veterinary Science, 9: Is Your Goat Feeling Green

2009· book· en· W2781872740 on OpenAlexaff
Martin H. Smith, Cheryl L. Meehan, Justine Ma, H Steve Dasher, Joe D Camarillo, Adele B. Moses, Joyce Wong

Bibliographic record

Venuenot available
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsRoyal Alberta Museum
Fundersnot available
KeywordsFeelingVeterinary medicinePsychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Overview and Background InformationPrevention is the key in keeping a goat healthy.A goat needs the proper types and amounts of food, clean water, well-ventilated housing, exercise, attention, space, and necessary vaccinations.Like any other animal, the best way to help keep a goat healthy is to notice any signs of illness as early as possible.The sooner the goat gets the treatment it needs, the more likely it is to recover fully.Daily observations of your goat will help you detect any physical or behavioral changes that could be a sign of illness.Goats are intelligent and curious animals that are often kept as pets.They have excellent balance, which enhances their already great climbing ability.They are adaptable to a variety of climates and can be found in most regions of the world.Goats are relatives of sheep and, like sheep, live in herds.Male goats are called bucks; females are called does; young goats are called kids.Goats are ruminants, meaning they have 4-chambered stomachs like cows.These chambers allow goats to regurgitate and then redigest their food.Goats were domesticated around 8,000 years ago in the Middle East for their hair, meat, and milk.Back then, goat skin was also used for parchment (a material on which to write or paint) and wine containers.Today, goats are raised for many of the same reasons: hair, meat, and milk.Goat's milk is becoming popular because it is more easily digested than milk from a cow, and it is used to make several kinds of cheese, such as feta.Several breeds are raised specifically as milk goats, such as Saanens, LaMancha, and Alpine.Other goat breeds, such as the Boer, are bred specifically for their meat.Angora and Pygora goats are both raised for their hair, which is used to make mohair and cashmere sweaters, respectively.Goats are also used for weed control since they enjoy eating woody shrubs and weeds and have quite an ability to climb.Contrary to popular belief, goats will not eat anything and everything: they are actually quite particular eaters.However, be aware that although they are picky, they may eat plants that are poisonous to them.Common plants that cause problems include azaleas (Rhododendron spp.), wild mustard, acorns, wilted leaves of any stone fruit tree (like cherries and Is Your Goat Feeling Green?Y o u t h D e v

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)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.065
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0650.030

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 head0.162
GPT teacher head0.299
Teacher spread0.137 · 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
GenreOther

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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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