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Record W1963932231 · doi:10.1139/x03-169

Cadmium-containing wood ash in a pine forest: effects on humus microflora and cadmium concentrations in mushrooms, berries, and needles

2003· article· en· W1963932231 on OpenAlexvenueno aff
Jonna Perkiömäki, Oili Kiikkilä, Mikko Moilanen, Jorma Issakainen, Arja Tervahauta, Hannu Fritze

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsHumusCadmiumWood ashChemistryVacciniumHorticultureBotanyEnvironmental chemistryAgronomyBiologySoil waterEcology

Abstract

fetched live from OpenAlex

The cadmium (Cd) concentration of wood ash (1–30 mg·kg –1 ) is higher than allowed for agriculture fertilizers (3 mg·kg –1 ). Therefore, the objectives of this field study were to test if the Cd of wood ash has the potential to affect the coniferous forest humus microflora and if Cd enters the human food chain. These objectives were tested with ash (A) and Cd-spiked ash (ACd, 400 mg Cd·kg –1 ) at a fertilization rate of 3 t·ha –1 . Microbial community structure, respiration, needle litter decomposition, growth rates and Cd tolerance of bacteria, and the bioavailability of Cd were measured. Also, Cd concentrations of humus, soil percolation water, mushrooms, fruits and leaves of berries, and needles were determined. The amount of Cd in the percolation water or bioavailable Cd, measured with a bacterial biosensor, and Cd tolerance of bacteria did not increase, although the ACd treatment increased the amount of humus total and extractable Cd. Only the ACd and not the A treatment caused Cd concentration increment in Lactarius rufus and berries of Empetrum nigrum. In spite of the high Cd concentration of the spiked ash, it did not have harmful effects on humus microorganisms during this 4-year study. Thus, wood ash is safe to use as a fertilizer in forests.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.286
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations32
Published2003
Admission routes1
Has abstractyes

Explore more

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