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Record W4393930501 · doi:10.32388/p4r72s

Review of: "Mineral stabilities in soils: how minerals can feed the world and mitigate climate change"

2024· peer-review· en· W4393930501 on OpenAlexaff
Rafael M. Santos

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMineralSoil waterEarth scienceEnvironmental scienceGeochemistryClimate changeEnvironmental chemistryNatural resource economicsChemistrySoil scienceGeologyOceanographyEconomics

Abstract

fetched live from OpenAlex

Potential competing interests: No potential competing interests to declare.Enhanced rock weathering (ERW) is potentially a climate change mitigation wedge, and the current state of the science is leading to increasing deployment of this technology in croplands, pasture lands, and forested lands.The article by Manning (2022) is a literature review that aimed to cover the mineral weathering aspects of ERW by compiling relevant literature available at the time of writing.Mineral weathering is a mature science, so it is not surprising that the review included papers spanning over a century, from 1922 until 2022.The review covers several aspects of geochemistry and some aspects of mineralogy, including: ΔG rxn (Figure 1) of several weathering reactions (Table 1);The expected solubility of Si and Al as a function of pH (Figure 2); Mineral weathering data in Figure 3 and Table 2 ( which contains a critical mistake, to be discussed below); SEM images exemplifying the microbial role during weathering in soils (Figure 4); A schematic illustration of the oxygen and carbon isotope compositional range of soil carbonates (Figure 5); and A unique analysis of the expected silicon release from silicate weathering (rock-forming minerals in Figure 6 and clay minerals in Figure 7) compared to the expected Si intake of wheat during a crop season.The review article by Manning (2022) is timely and largely useful for researchers and practitioners involved in ERW.However, as it contains one important mistake, the rest of this post-publication review will focus on rectifying this.Below is the main issue found in Manning (2022).The original Figure 3 (and corresponding values also shown in Table 2) showed values that were reported to be "mineral dissolution rates", in log units.However, those values are in fact mineral dissolution rate constants (in log units), which we can term k.The values of log k are meant to be used in equations developed by Palandri and Kharaka (2004) to first calculate log A values, and then calculate log W r values, where W r is the "mineral dissolution rate".In fact, W r is a function of pH and temperature, so W r values can be readily calculated for conditions expected to be found in soils, while k being a constant, actually represents a theoretical weathering rate at pH of 0 (zero) and 25°C.Manning (2022) actually realizes this later in the review, when presenting the data of Figures 6 and 7, but when presenting Figure 3 and Table 2, which are more likely to be used by readers, the correct terminology and meaning of the presented values is missing.To rectify this, I have reproduced Figure 3 below, with the original figure on the left, now clearly indicating that those are log k values for the acid mechanism, and a modified figure on the right, where the values are calculated log W r 's based on pH of 6.0 and temperature of 20°C and using the most appropriate Qeios, CC-BY 4.0

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)0.013
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0040.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0540.033

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 head0.287
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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