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Record W2159279198 · doi:10.1139/x05-127

Soil microclimate and nitrogen availability 10 years after mechanical site preparation in northern British Columbia

2005· article· en· W2159279198 on OpenAlexvenueaboutno aff
M. Derek MacKenzie, Margaret Schmidt, Lorne Bedford

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceSoil fertilityNutrientAgronomyNitrogenGrowing seasonMicroclimateSoil testSoil waterAnimal scienceEcologyChemistrySoil scienceBiology

Abstract

fetched live from OpenAlex

Mechanical site preparation (MSP) changes the distribution and character of forest floor and mineral soil and may affect soil nutrient availability, soil water content, and soil temperature. The effects of different kinds of MSP were compared to a control in the tenth growing season at two research sites in northern British Columbia. To compare MSP results with those of the natural disturbance regime, a burned windrow treatment was also included in the analysis. The bedding plow, fire, and madge treatments all had significantly greater crop-tree growth compared to the control. The bedding plow and madge treatments had significantly lower soil bulk density, higher soil temperature, and lower soil water throughout the growing season compared with that of the control. The bedding plow also resulted in significantly higher concentrations of total carbon, total nitrogen, NH 4 + , and NO 3 – than that of the control, at both the 0–10 and 10–20 cm depths. The madge rotoclear resulted in significantly greater potential mineralizable N than that of the control. Ionic resins bags, installed for one growing season, did not show any significant treatment differences in available soil nitrogen. MSP did not reduce soil fertility on these sites when compared with an untreated control, but it is difficult to say that it improved it.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.258
Teacher spread0.242 · 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.

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

Citations35
Published2005
Admission routes2
Has abstractyes

Explore more

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