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Record W2944936886

Záchranné programy a opatření na ochranu zemních veverek

2010· dissertation· cs· W2944936886 on OpenAlexaboutno aff
Kristýna Novotná

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2010
Typedissertation
Languagecs
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Unfortunately, the number of endangered species is still increasing, in most cases due to the human activities. The main aim of the protection of species is to provide conditions for survival of endangered species in nature. However, for some species the common instruments of protection (such as protection of habitat) are not sufficient and it is necessary to use some special methods. For these species are organized so-called action plans. Among such species belongs the European Ground Squirrel (Spermophilus citellus). We can find also other endangered species within ground squirrels from the tribe Marmotini Pocock, 1923 as well. These species need the special protection methods, like captive breeding, repatriation and translocation of individuals or special management of the habitat. In this work are processed data about protection of genera Cynomys, Marmota and Spermophilus, especially White-tailed Prairie Dog (C. leucurus), Utah Prairie Dog (C. parvidens), Alpine Marmot (M. marmota), Vancouver Island Marmot (M. vancouverensis), Idaho Ground Squirrel (S. brunneus), European Ground Squirrel (S. citellus), Franklin's Ground Squirrel (S. franklinii), Speckled Ground Squirrel (S. suslicus) and Washington ground squirrel (S. washingtoni). Actual status and cause of endangerment are given for each...

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)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.090
Threshold uncertainty score0.301

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0900.031

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 head0.008
GPT teacher head0.239
Teacher spread0.232 · 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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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Same venueDigital Repository (National Repository of Grey Literature)Same topicFood Waste Reduction and SustainabilityFrench-language works237,207