MétaCan
Menu
Back to cohort
Record W4392646304 · doi:10.3390/f15030517

Research Progress on Forest Eco-Product Value Realization and Eco-Industry: The Inspiration for Planted Forests in Karst Desertification Control

2024· article· en· W4392646304 on OpenAlexaboutno aff
Zefu Zhang, Kangning Xiong, Yu Zhang, Youze Ning

Bibliographic record

VenueForests · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
FundersU.S. Forest Service
KeywordsKarstDesertificationChinaGeographyValue (mathematics)Product (mathematics)EcologyEnvironmental scienceForestryEnvironmental resource managementEnvironmental protectionMathematicsBiologyStatisticsArchaeology

Abstract

fetched live from OpenAlex

Karst desertification (KD) is a phenomenon in which the surface of the karst region presents a landscape similar to desertification, which restricts the social development of the karst region. How to develop values based on karst desertification control is the focus of current research, with the continuous promotion of karst desertification control (KDC). Planted forests for KDC are an important part of the value realization of ecological products (EPs) and ecological industry (EI) in karst areas. The statistical analysis of 265 relevant papers in this paper shows the following: (1) the literature focuses on EP and EI research; (2) the overall research shows a year-on-year growth trend. It includes three stages: budding (2001–2006), fluctuating growth (2007–2015) and rapid development (2016–2023), with 6, 58 and 211 articles published in each stage, respectively; (3) China is the country with the largest number of articles published, followed by the United States and then Canada, accounting for 47.74%, 11.93% and 6.17%. This paper summarizes the landmark results and key scientific issues to be solved in the study directions of EP supply capacity, EP value accounting, eco-products value realization (EPVR) and EI, taking into account the above results.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.336
Teacher spread0.293 · 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 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

Citations7
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueForestsSame topicWildlife-Road Interactions and ConservationFrench-language works237,207