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Record W1479402371 · doi:10.2136/sssabookser8.c13

Speciation of Metals in Soils

2005· book-chapter· en· W1479402371 on OpenAlex
Darryl R. Roberts, Maarten Nachtegaal, Donald L. Sparks

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSoil Science Society of America book series · 2005
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsWater and Earth Science Associates (Canada)
Fundersnot available
KeywordsGenetic algorithmSoil waterSorptionEnvironmental chemistryEnvironmental scienceEarth scienceMetalChemistrySoil scienceEcologyGeologyAdsorptionBiology

Abstract

fetched live from OpenAlex

Given the potential of soils to cycle metals between the various phases, metal speciation in soils can be used to assess regional and global metal cycling in many environmentally relevant materials. Metals are present in soils as a result of both natural and anthropogenic processes, and separating out the two sources is often not a trivial task. Determining metal speciation in soils can be quite complex as thermodynamic models may give suggestions as to the possible species to expect in a system, but metal species are usually controlled by kinetics of the reactions. Sorption reactions of metals in soils to a large extent dictate their mobility, fate, and bioavailability and are therefore vital to understand when attempting to understand metal speciation. The use of synchrotron light sources to address environmental issues has provided insight into the reaction mechanisms of heavy metals at interfaces between sorbent phases found in soils and the soil solution.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.010
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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