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Enregistrement W4393662149 · doi:10.5281/zenodo.2548630

Large-scale and fine-grained phenological stage annotation of herbarium specimens datasets

2019· dataset· en· W4393662149 sur OpenAlexaff
Titouan Lorieul, Katelin D. Pearson, Elizabeth R. Ellwood, Hervé Goëau, Jean‐François Molino, Patrick W. Sweeney, Jenn Yost, Joel L. Sachs, Erick Mata‐Montero, Gil Nelson, Pamela S. Soltis, Pierre Bonnet, Alexis Joly

Notice bibliographique

RevueFigshare · 2019
Typedataset
Langueen
DomaineEnvironmental Science
ThématiqueSpecies Distribution and Climate Change
Établissements canadiensAgriculture and Agri-Food Canada
Organismes subventionnairesnon disponible
Mots-clésHerbariumPhenologyAnnotationScale (ratio)Stage (stratigraphy)Computer scienceGeologyGeographyCartographyPaleontologyArtificial intelligenceBiologyBotany

Résumé

récupéré en direct d'OpenAlex

This upload is constituted of four datasets of specimens from American herbaria covering different levels of information precision and different floras - from temperate to equatorial. Three of these datasets consist of selected specimens from herbaria located in different geographic and environmental regions. Each specimen of these three datasets was annotated with the following fields: family, genus, species name, fertile / non-fertile, presence / absence of flower(s), presence / absence of fruit(s). The resulting dataset was composed of 163,233 herbarium specimens belonging to 7,782 species, 1,906 genera, and 236 families. Specimens were annotated as “fertile” if any reproductive structures were present, such as sporangia (ferns), cones (gymnosperms), flowers, or fruits (angiosperms). Non-fertile specimens were those that lacked any reproductive structures. The fourth dataset consists of 20,371 herbarium specimens from 11 genera in the sunflower family (Asteraceae). The main difference in this dataset is that it is annotated with fine-grained phenophase scores rather than presence/absence attributes (see description below). Each of these datasets is described below: NEVP: this dataset of New England vascular plant (NEVP) specimens was produced by members of the Consortium of Northeastern Herbaria. The dataset comprises 42,658 digitized specimens that belong to 1,375 species and come from several North American institutions. Most of the specimens in this dataset are from the north-temperate region of the northeastern United States. FSU: this dataset was produced by the Florida State University's Robert K. Godfrey Herbarium (FSU), a collection that focuses on northern Florida and the U.S. Southeast Coastal Plain, one of North America's biodiversity hotspots. This dataset contains 54,263 digitized herbarium specimen records that belong to 3,870 species, making it the taxonomically richest dataset in this study. Most species in this dataset grow under subtropical or warm temperate conditions in the southeastern region of the United States. CAY: this dataset comes from the IRD’s Herbarium of French Guiana (CAY). CAY is dedicated to the Guayana Shield flora, with a strong focus on tropical tree species. This dataset is composed of 66,312 herbarium specimens that belong to 3,024 species. All digitized specimens of this herbarium are accessible online. Most specimens were collected in the tropical rainforests of French Guiana, with the remaining specimens coming mostly from Suriname and Guyana. PHENO: this dataset includes 20,371 herbarium specimens of 139 species in the Asteraceae produced in a study of phenological trends in the U.S. Southeast Coastal Plain. The dataset is composed of specimen records from 57 herbaria. Each recorded specimen was annotated for quartile percentages (0, 25, 50, 75, or 100%) of (i) closed buds, (ii) buds transformed into flowers, and (iii) fruits. According to the distribution of these three categories for each specimen, a phenophase code was computed. Datasets format These datasets are grouped in 3 tasks: fertility detection flowers and/or fruit detection phenophase classification The first 2 tasks are carried on the first 3 previous datasets and thus are based on the same set of images, unlike the third task which has its own disjoint set of images. This is why the dataset is presented into two separated files, one for each set of images. Fertility detection & flower/fruit detection These tasks are contained into the herbarium_fertility_annotations.zip archive. It consists of 3 files: metadata.csv: general information about all the herbarium specimens for these tasks id: specimen identifier collection: which of NEVP, FSU or CAY does the specimen come from herbarium: institution of origin of the specimen, especially for NEVP collection clade, family, genus, species: classification of the specimen URL: URL of the scan fertility_task.csv: specific information regarding the fertility detection task id: specimen identifier is_fertile: True if the specimen has an expression of fertility, False otherwise train_test_set: which subset does the specimen belong to; possible values are: train, random_test, species_test and herbarium_test flower_fruit_task.csv: specific information regarding the flower/fruit detection task id: specimen identifier, note that in this case not all the specimen described in metadata.csv are included in this task has_flower: True if the specimen has at least one flower, False otherwise has_fruit: True if the specimen has at least one fruit, False otherwise train_test_set: which subset does the specimen belong to; possible values are: train, random_test, species_test and herbarium_test Phenophase classification These tasks are contained into the herbarium_asteraceae_phenophase_annotations.zip archive. It consists of a single file: annotations.csv: id: specimen identifier URL: URL of the scan genus: genus of the specimen phenophase: integer from 1 to 9 describing the phenophase of the specimen train_test_set: which subset does the specimen belong to; possible values are: train and test Additional ressources More information can be found in the related paper: Lorieul, T., K. D. Pearson, E. R. Ellwood, H. Goëau, J.-F. Molino, P. W. Sweeney, J. M. Yost, J. Sachs, E. Mata-Montero, G. Nelson, P. S. Soltis, P. Bonnet, and A. Joly. 2019. Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Applications in Plant Sciences 7(3): e1233. For an example of usage of these datasets as well as a baseline, see: http://doi.org/10.5281/zenodo.2549996

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,065

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0060,006
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0200,025

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,051
Tête enseignante GPT0,280
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2019
Routes d'admission1
Résumé présentoui

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