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Record W4200276890 · doi:10.1016/j.dib.2021.107677

Data set showing the development of a hyperspectral imaging technique using LA-ICP-MS to determine the spatial distribution of nutrients in soil cores

2021· article· en· W4200276890 on OpenAlexafffund
Muhammad Zaeem, Muhammad Nadeem, Thu Huong Pham, Waqar Ashiq, Waqas Ali, Syed Shah Mohioudin Gillani, Eric R.D. Moise, Heather Leier, Vanessa Kavanagh, Lakshman Galagedara, Mumtaz Cheema, Raymond Thomas

Bibliographic record

VenueData in Brief · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsNatural Resources CanadaUniversity of CalgaryUniversity of GuelphGovernment of Newfoundland and LabradorCanadian Forest ServiceMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaResearch and Development Corporation of Newfoundland and LabradorDepartment of Fisheries and Land ResourcesGrain Research and Development Corporation
KeywordsHyperspectral imagingSpatial distributionEnvironmental scienceNutrientData setBiogeochemistryRemote sensingRhizosphereSoil scienceComputer scienceEnvironmental chemistryChemistryGeographyEcologyGeologyBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

This data in brief article represents the data set associated with a research article published in Geoderma [1]. The data set represents figures showing the spatial distribution of selected macro and micronutrients, and their quantification in different crop or nutrient management systems practiced in the boreal ecosystem. Spatial distribution of nutrients was measured by laser ablation inductively coupled plasma mass spectrometry (LA‒ICP‒MS), using the new techniques we developed to visualize nutrient distribution in intact soil cores representative of the root rhizosphere. This data article supports the findings published in the main article [1]. This work also demonstrates that LA-ICP-MS is a valuable technique to image the spatial distribution of macro and micronutrients in intact soil cores as affected by different crop management practices.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.004

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 head0.086
GPT teacher head0.318
Teacher spread0.231 · 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 designBench or experimental
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

Citations2
Published2021
Admission routes2
Has abstractyes

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