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Record W2620113994 · doi:10.7910/dvn/h1jaze

Replication Data for: Timber yield from smallholder agroforestry systems in Nicaragua and Honduras

2016· dataset· en· W2620113994 on OpenAlexaff
Kauê Detlefsen De Sousa

Bibliographic record

VenueHarvard Dataverse · 2016
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsCanadian AIDS Treatment Information Exchange
Fundersnot available
KeywordsAgroforestryReplication (statistics)Yield (engineering)GeographyEnvironmental scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Permanent sample plots (PSP) were established—in 2010 in Nicaragua and in 2011 in Honduras—in agroforestry systems with different timber species. These systems include: (i) silvopastoral systems (SPSs) with 0.5 ha each one; (ii) coffee farms, 0.1 ha each; (iii) cocoa farms, 0.1 ha each; and (iv) living fences (LFs) each 100 m long. Information from communities and farmers involved in CATIE’s projects (Mesoterra, CAFNET and Finnfor) was used to identify and select the farms, using the following criteria: (i) be a smallholder farm; (ii) have potential for timber production, with at least one commercial timber species; and (iii) farmer be available and willing to participate in the research. Diameter at breast height (DBH), commercial and total height, stem form, mortality and natural regeneration were evaluated in forestry inventories performed in each PSP in 2010, 2011, 2012 and 2014 in Nicaragua; and in 2011, 2012 and 2014 in Honduras. Paper-based data collection was used between 2010 and 2012; a smartphone-based method was adopted in 2014 to assist in PSP measuring in both regions.

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)0.003
metaresearch head score (Gemma)0.013
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.012

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 head0.060
GPT teacher head0.269
Teacher spread0.208 · 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
GenreDataset

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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