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Record W6987073766

Sears to close more stores, sales continue fall in 2Q

2017· other· en· W6987073766 on OpenAlexaboutno aff

Bibliographic record

VenueInternet Archive (Internet Archive) · 2017
Typeother
Languageen
FieldMedicine
TopicLegal Cases and Commentary
Canadian institutionsnot available
Fundersnot available
KeywordsCompetition (biology)FellClosing (real estate)Quarter (Canadian coin)Key (lock)
DOInot available

Abstract

fetched live from OpenAlex

Sears continued to struggle in its second quarter with declining sales amid heightened competition from the likes of Walmart to Amazon. It now says it will close even more stores Sears plans to close more stores, as it deals with declining sales amid heightened competition from companies like Walmart and Amazon.The company, which operates Sears and Kmart stores, has been trying to cut costs by closing stores, including 180 this year.It had already had plans to cut another 150 stores.It now plans to close an additional 28 Kmart stores.Sales continued to decline for the retailer in the second quarter.It says its second-quarter loss narrowed to $251 million, or $2.34 per share.Revenue fell 23 percent to $4.37 billion in the period.Sales at stores open at least a year, a key measure of a retailer's health, dropped 11.5 percent.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.097
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0120.004
Scholarly communication0.0060.003
Open science0.0020.003
Research integrity0.0160.010
Insufficient payload (model declined to judge)0.0920.023

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 head0.285
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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