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Record W2112167211 · doi:10.1139/x03-111

Initial responses of phosphorus biogeochemistry to calcium addition in a northern hardwood forest ecosystem

2003· article· en· W2112167211 on OpenAlexvenueno aff
Isabella Fiorentino, Timothy J. Fahey, Peter M. Groffman, Charles T. Driscoll, Christopher Eagar, Thomas G. Siccama

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsBiogeochemistryCyclingForest floorBiogeochemical cyclePhosphorusWatershedExperimental forestEnvironmental scienceEnvironmental chemistryNutrient cycleChemistryEcosystemBiomass (ecology)Soil horizonAnimal scienceEcologySoil waterSoil scienceBiologyForestry

Abstract

fetched live from OpenAlex

We measured changes in P pools and cycling 1 year after a Ca fertilization treatment at the Hubbard Brook Experimental Forest in central New Hampshire. We hypothesized that by increasing soil pH and available Ca, the treatment would change the amount of readily available P in the forest floor and the biogeochemical cycling of P in the forest ecosystem. One year after the Ca addition, significant increases occurred in soil pH (one pH unit), resin-sorbed P (2- to over 10-fold), and the microbial respiratory quotient (27%) in the Oe horizon of the treated watershed compared with a reference watershed. Additionally, we observed significant increases in foliar P concentrations (20–133% across six species) and in P retranslocation in the treated watershed between pre- and post-Ca-addition years (p < 0.05). Foliar P was strongly correlated (r = 0.74) with resin-sorbed P. Microbial biomass P, microbial C to P ratios, and available organic and inorganic P fractions were lower in the Oe horizon of the treatment watershed than in the reference watershed, but no differences were observed in soil solution or fine root P concentration. Apparently, by changing soil pH, Ca addition increased rates of P cycling in forest floor horizons at Hubbard Brook Experimental Forest.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.291
Teacher spread0.256 · 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

Citations43
Published2003
Admission routes1
Has abstractyes

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