Conservation stories from the front lines
Notice bibliographique
Résumé
This Editorial is part of the Conservation Stories from the Front Lines CollectionThe stories of science are told many ways, in many places.Scientists share the ups and downs of the research process over raucous conference cocktails and long hours on the road, across lab benches and conference call lines, and around campfires after long days in the field.These stories underlie every scientific paper yet rarely appear alongside the tables and graphs.To read the often dull, sometimes tedious reports that fill the scientific record, you'd never know that science is a human endeavor, like any other, shaped by tragedy, comedy, and (mis)adventures.In this issue of PLOS Biology, we highlight the deeply human side of research in a new collection, "Conservation Stories from the Front Lines."These narratives present peer-reviewed and robust science but also include the muddy boots and bloody knees, ravaging mosquitoes, crushing disappointment, and occasional euphoria their authors experienced.We deliberately sought stories of triumphs and tragedies, successes and failures, and invited a diverse group of scientists to submit contributions written in their own voices.Rather than cling to a standard structure, we asked authors to choose their own format to best present their ideas, experiences, results, and conclusions in a style that is compelling, concise, and accessible.Our focus in this collection is conservation-science that speaks to the management and preservation of species and ecosystems.Contributions range from perspectives on an existing body of research to the presentation of novel research findings.Authors were encouraged to breathe life into their scientific stories by incorporating narrative elements such as characters, scenes, conflict, and resolution.Karen Lips describes the agony of watching the rainforest frogs she studied for years suddenly and mysteriously disappear [1].Nick Haddad shares epiphanies about the recovery of rare species gleaned from humbling struggles with his health [2].Elizabeth Hadly confesses her fear that the days when government leaders acted on evidence of human-driven planetary emergencies may be gone [3].Emmanuel Frimpong urges us to consider how the ecological role of an overlooked fish warrants a new approach to freshwater fish conservation [4].And Sergio Avila-Villegas reveals how a painful encounter with a jaguar changed the trajectory of his life and his life's work [5].Stories are powerful, even transformative.Most of us are aware of that power, based either on personal experience or on stories we know from the media and entertainment industries.But we can go beyond intuition and look to the scientific study of stories.Compared with argumentative or evidence-based communication, narratives focus on causal linkages among a sequence of events influenced by the actions of specific characters.They often carry an emotional punch and relate these events in a way that resonates with readers.As a result, narrative has the power to improve comprehension, increase topical interest, influence real-world beliefs, and achieve persuasive outcomes [6].
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,010 | 0,010 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,019 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,003 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».