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Record W2527840503 · doi:10.20361/g2tw36

Ecosystems by N. Finton

2016· article· en· W2527840503 on OpenAlexvenueno aff
Jackson BCR

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCreaturesEcosystemGeographyHabitatEcologyEnvironmental ethicsNatural (archaeology)ArchaeologyBiology

Abstract

fetched live from OpenAlex

Finton, Nancy. Ecosystems. Washington, D.C: National Geographic, 2004. Print.Life science Eco-system by Nancy Finton and published by the National Geographic Society. The book is about the ecosystem and some interesting facts about animals and the North American landscape and ecosystem. This book also tells facts and the near extinction of the black footed ferret one of the rarest creatures in North America. This book is also about how the ecosystem and the food web works and these fun facts are awesome and I like science.I love the facts and pictures in this book because they are scientifically factual. I also love the meaning and facts of this book and it’s about the ecosystem and food web also animals that live in the great prairies of North America like the Pronghorn antelope which eats sage and the nearly extinct black footed ferret which feeds on prairie dogs. About 271 black footed ferrets have been raised since 1987 to 2001 and 202 black footed ferrets have been released since 1987 to 2001 what a beautiful job people did.I have no complaints about this book except there is little information about the ecosystem and I hope the author Nancy Finton adds more information and detail to this book. Not a lot of information about the different ecosystems and habitats from different countries around the world and there is way too much about prairie dogs like really I want to know lots but too much information about prairie dogs![Highly Recommended: 4 out of 5 stars]Reviewer: JacksonHello my name is Jackson and I like books about science and Nuclear physics and I think reading is important because we and our children will need knowledge and know everything we need to know so our lives can be easier in the future.

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: Review · Consensus signal: none
Teacher disagreement score0.232
Threshold uncertainty score0.777

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2320.222

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 head0.003
GPT teacher head0.201
Teacher spread0.199 · 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
GenreReview

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
Published2016
Admission routes1
Has abstractyes

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