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Record W6888769874 · doi:10.21966/6xx9-1y19

Orthophoto High Compression 0.25m resolution Mosaic - 2012 - Calvert Island - British Columbia - Canada

2015· dataset· en· W6888769874 on OpenAlexaboutno aff

Bibliographic record

VenueHakai Institute · 2015
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsOrthophotoMosaicHigh resolutionCompression (physics)Image resolutionHyperspectral imaging

Abstract

fetched live from OpenAlex

This lightweight mosaic of 2012 orthophotos covering the entire Calvert Island at 0.25m resolution was created from 589 individual orthophotos covering the entire extent of Calvert Island. JPEG200 compression has been utilized to create this mosaic dataset. This mosaic was created with the intent to provide Hakai researchers, staff and collaborators with a "lightweight" single imagery file which provides coverage for the entire Calvert Island at very high resolution. By employing the JPEG2000 compression, the mosaic has been reduced to >1/4 of the total size that would be obtained by mosaicking all the individual orthophotos in their native TIFF format. While it is expected that such a high compression mechanism would have an impact on the quality of the imagery, the end result in this mosaic is hardly distinguishable visibly from the original air photos. The air photos / orthophotos were acquired by Hakai through Terra Remote Sensing - CEDD Lab UVic - Hyperspectral Lab UVic. These orthophotos have the highest available spatial resolution that covers the entire Calvert / Hecate area. Major efforts were used to process individual orthophotos to have a seamless look by the CEDD Lab UVic.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.232
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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