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Record W3045540576

Hard Hill experimental plots on Moor House – Upper Teesdale National Nature Reserve - A review of the experimental set up (NECR321)

2020· other· en· W3045540576 on OpenAlexfundno aff
Ben Clutterbuck, Robert O. Lindsay, Jordan Clough

Bibliographic record

VenueUEL Research Repository (University of East London) · 2020
Typeother
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
FundersNatural EnglandNatural Environment Research CouncilTrent UniversityUK Research and InnovationNottingham Trent UniversityUniversity of East London
KeywordsSet (abstract data type)GeographyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Hard Hill experimental plots on Moor House -Upper Teesdale National Nature ReserveA review of the experimental set up Foreword Natural England commission a range of reports from external contractors to provide evidence and advice to assist us in delivering our duties.The views in this report are those of the authors and do not necessarily represent those of Natural England. BackgroundThe Hard Hill experimental plots on Moor House NNR were established in 1954 to investigate the interaction between prescribed burning and grazing upon blanket bog vegetation in the North Pennines.The treatments (burning on short (10year) and longer (20-year) rotations, no-burning, grazing and no-grazing) have been continued and since their initiation and the experiment has been the subject of numerous investigations.In recent years, there has been discussion as to how comparable the blocks and plots are, and this project was initiated to investigate the similarities and differences between the blocks and plots to help guide new research in addition to revisiting previous findings.This report should be cited as: CLUTTERBUCK, B., LINDSAY, R, CHICO, G., CLOUGH, J. 2020.Hard Hill experimental plots on Moor House -Upper Teesdale

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: Review · Consensus signal: none
Teacher disagreement score0.083
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.005

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 head0.079
GPT teacher head0.305
Teacher spread0.225 · 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

Citations2
Published2020
Admission routes1
Has abstractno

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