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Record W6913224774 · doi:10.5683/sp3/lglckw

In-situ Soil Temperature Data (2 and 10 cm) in Agro-forested Areas of St-Marthe and St-Maurice for 2020-21 and 2021-22

2024· dataset· en· W6913224774 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsBackscatter (email)Hydrology (agriculture)Temperature measurementMode (computer interface)Change detectionSoil testSoil waterInstrumentation (computer programming)

Abstract

fetched live from OpenAlex

In this dataset, In-situ soil temperature measurements were collected for two consecutive years in Agro-forested areas of St-Maurice and St-Marthe located in south Québec (Canada), from mid-October to the end of April, covering the periods of 2020-21 and 2021-22. 8 and 10 temperature plots were instrumented in St-Maurice and St-Marthe, respectively, to monitor soil freeze-thaw (FT) states. At each plot, five soil pits equipped with two soil temperature sensors at near-surface (2 cm) and 10 cm depths were installed along a cross shape with 5 m between each soil pit. We used the standard normal distribution, which is a continuous probability distribution, to determine the probability of soil freezing at each instrumented plot. As part of this dataset, Sentinel1 Interferometric Wide Swath Mode (IW) imagery was used, using both ascending and descending orbits during the time frame of early October to early June, covering the periods of 2020-2021 and 2021-2022. Three change detection algorithms were also calculated to retrieve the FT state for both VV- and VH-polarizations at the studied plots including freeze-thaw index (FTI) algorithm, backscatter differences (Delta), and exponential freeze-thaw algorithm (EFTA). For more detail please refer to the README file in dataset.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.370
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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 teacher head, not a consensus.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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