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Record W3015965191 · doi:10.17504/protocols.io.8jxhupn

Postex System User Guide v1

2019· preprint· en· W3015965191 on OpenAlexaffabout
Anna L. Crofts

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTree canopyGeographyPlot (graphics)Tree (set theory)CanopyRemote sensingObject (grammar)ForestryComputer sciencePosition (finance)CartographyMathematicsArtificial intelligenceStatisticsArchaeology

Abstract

fetched live from OpenAlex

The Postex system (Haglöf Sweden AB, Långsele, SE) is used to position objects–most often, and in our case, trees–within sample plots. The positions of individual trees are easily obtained and accurate, making the Postex system suitable for collecting ground-based measurements that compliment aerial surveys. Using ultrasound technology, the three stationary transponders, centered around the plot centre, measure the distance between each other and the handheld device, located at the object (i.e., tree) of interest and determines the position of the object in relation to the plot center. The Postex system was used during the canopy tree surveys of the forested Canadian Airborne Biodiversity Observatory (CABO) study sites: Parc national du Mont-Mégantic and Parc national du Mont-Saint-Bruno. Here, we provide a stepwise guide to using the Postex system, specifically related to how it was used for CABO canopy tree surveys (for full description of the Postex system please refer to the Postax version 2.2 and DP II user manuals, see pdfs attached below).

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)0.009
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: Software · Consensus signal: Software
Teacher disagreement score0.529
Threshold uncertainty score0.672

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0040.004
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5290.512

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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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
GenreSoftware

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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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