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Record W2033777969 · doi:10.1073/pnas.1221029110

Biogeochemistry of beetle-killed forests: Explaining a weak nitrate response

2013· article· en· W2033777969 on OpenAlexaboutno aff
Charles C. Rhoades, James H. McCutchan, Leigh Cooper, David W. Clow, Thomas M. Detmer, Jennifer S. Briggs, John D. Stednick, Thomas T. Veblen, Rachel M. Ertz, Gene E. Likens, William M. Lewis

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPinus contortaNitrateEnvironmental scienceVegetation (pathology)Bark beetleEcologyDeposition (geology)CanopyBiogeochemistryHydrology (agriculture)BiologyGeology

Abstract

fetched live from OpenAlex

A current pine beetle infestation has caused extensive mortality of lodgepole pine (Pinus contorta) in forests of Colorado and Wyoming; it is part of an unprecedented multispecies beetle outbreak extending from Mexico to Canada. In United States and European watersheds, where atmospheric deposition of inorganic N is moderate to low (<10 kg⋅ha⋅y), disturbance of forests by timber harvest or violent storms causes an increase in stream nitrate concentration that typically is close to 400% of predisturbance concentrations. In contrast, no significant increase in streamwater nitrate concentrations has occurred following extensive tree mortality caused by the mountain pine beetle in Colorado. A model of nitrate release from Colorado watersheds calibrated with field data indicates that stimulation of nitrate uptake by vegetation components unaffected by beetles accounts for significant nitrate retention in beetle-infested watersheds. The combination of low atmospheric N deposition (<10 kg⋅ha⋅y), tree mortality spread over multiple years, and high compensatory capacity associated with undisturbed residual vegetation and soils explains the ability of these beetle-infested watersheds to retain nitrate despite catastrophic mortality of the dominant canopy tree species.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.522

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.257
Teacher spread0.238 · 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 designBench or experimental
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

Citations92
Published2013
Admission routes1
Has abstractyes

Explore more

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