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Record W4283745178 · doi:10.32747/2022.7807781.ch

Seedlot Selection Tool Guidebook for USFS Region 6 Silviculturists

2022· report· en· W4283745178 on OpenAlexaff
Andrew J. Bower, Vicky J. Erickson, Holly R. Prendeville, J. Brad St. Clair, Gwynne Corrigan, Kai Foster, Gladwin Joseph, Nikolas Stevenson-Molnar, Deanne DiPietro

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsSt. Clair College
FundersU.S. Forest ServiceSmithsonian Conservation Biology InstituteU.S. Department of Agriculture
KeywordsReforestationSelection (genetic algorithm)ForestryEnvironmental resource managementEnvironmental scienceGeographyAgroforestryComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The Seedlot Selection Tool (SST) - Guidebook offers six examples that demonstrate the basic operation and settings options for the SST, as well as guidance on use of the application in different reforestation scenarios typically encountered on National Forest System lands in the USFS Pacific Northwest Region 6. The guidebook will help Region 6 silviculturists and reforestation specialists use the SST in planning their nursery and reforestation activities while considering current and future climate conditions.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.318
Threshold uncertainty score0.973

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.3180.143

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 head0.052
GPT teacher head0.285
Teacher spread0.233 · 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.

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
Published2022
Admission routes1
Has abstractyes

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