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Record W2888986862 · doi:10.1016/j.dib.2018.08.095

Intra-annual raw basal area increments (early-wood and late-wood) of Pinus nigra subsp. laricio Poiret trees from southern Italy at the pines׳ mesic to xeric distribution range

2018· article· en· W2888986862 on OpenAlexfundno aff
Gianluigi Mazza, Dimitrios Sarris, Ugo Chiavetta, Rossana Monica Ferrara, Gianfranco Rana

Bibliographic record

VenueData in Brief · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
FundersProgramma Operativo Nazionale Ricerca e CompetitivitàEuropean Regional Development FundMinistero dell’Istruzione, dell’Università e della RicercaCanadian Association of Palynologists
KeywordsDeserts and xeric shrublandsSubspeciesPinus <genus>Range (aeronautics)TaxonBotanyBiologyGeographyEcologyForestryHabitat

Abstract

fetched live from OpenAlex

This article contains tree rings data related to the research article entitled “An intra-stand approach to identify intra-annual growth responses to climate in Pinus nigra subsp. laricio Poiret trees from southern Italy” (Mazza et al., 2018). Most dendroclimatological studies on black pine have been conducted on the P. nigra subsp. nigra, while only few results on climate-growth relationships are available for other taxa such as P. nigra subsp. laricio, which has the narrowest distribution range of the collective species P. nigra . This data article provides tree rings data for the subsp. laricio at an intra-annual growth level, distinguishing early-wood (EW) and late-wood (LW), from an even aged forest stand from the Sila mountain area within the subspecies mesic to xeric distribution range.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.237
Teacher spread0.213 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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