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Record W2031860171 · doi:10.1080/01431160410001716923

Mapping insect‐induced tree defoliation and mortality using coarse spatial resolution satellite imagery

2005· article· en· W2031860171 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Remote Sensing · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsVegetation (pathology)Satellite imageryEnvironmental scienceRemote sensingSpatial ecologySatelliteAerial imageryPhysical geographyNormalized Difference Vegetation IndexGeographyEcologyBiologyClimate change

Abstract

fetched live from OpenAlex

Insect‐induced defoliation causes significant timber and carbon losses in many forested countries. The purpose of this investigation was to examine the potential use of coarse spatial resolution satellite imagery for mapping tree defoliation and mortality caused by a large insect infestation. We examined 1 km multi‐temporal SPOT Vegetation (VGT) data over a coniferous forest region in Quebec, Canada that was severely defoliated during 1998–2000 by the eastern hemlock looper. A logistic regression model based on satellite change metrics was developed to map defoliation and mortality. The results suggest that coarse imagery is effective for mapping large‐scale conifer forest mortality caused by insects, and could also be useful for near real‐time monitoring of severe defoliation, although with 2–3 times greater errors of commission.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.530

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)0.0000.000

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 head0.035
GPT teacher head0.285
Teacher spread0.250 · 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