MétaCan
Menu
Back to cohort

Research Priorities for Neotropical Dry Forests<sup>1</sup>

2005· article· en· W3199477407 on OpenAlex
Arturo Sánchez‐Azofeifa, Maurício Quesada, Jon Paul Rodrı́guez, Jafet M. Nassar, Kathryn E. Stoner, Alicia Melgoza‐Castillo, Theresa Garvin, Eglée L. Zent, Julio Calvo‐Alvarado, Margaret Kalácska, Laurie Fajardo, John A. Gamon, Pablo Cuevas‐Reyes

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.

Bibliographic record

VenueBiotropica · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTropical and subtropical dry broadleaf forestsNexus (standard)GeographyCitizen scienceTemperate rainforestTropical forestEnvironmental resource managementEcologyAgroforestryEnvironmental scienceForestryEcosystemBiologyComputer science

Abstract

fetched live from OpenAlex

ABSTRACT Our understanding of the human and biophysical dimensions of tropical dry forest change and its cumulative effects is still in the early stages of academic discovery. The papers in this special section on Neotropical dry forests cover a wide range of sites and problems ranging from the use of multispectral and hyperspectral remote sensing platforms to the impact of hurricanes on tropical dry forest regeneration. Here, we present to the scientific community the results of a workshop on which research priorities for tropical dry forests were discussed. This discussion focuses on the need to develop linkages between remote sensing, ecological, and social science research. The incorporation of social sciences into ecological research could contribute dramatically to our understandings of tropical dry forests by providing important contextual information to ecologists, and by helping to develop an important science–policy–public nexus on which environmental management can succeed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.040
GPT teacher head0.281
Teacher spread0.241 · 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